\" PROMPT_DICT = { \"prompt_input\": ( \"Below is an instruction that describes a task, paired with an input that provides further context. \" \"Write a response that appropriately completes the request.\\n\\n\" \"### Instruction:\\n{instruction}\\n\\n### Input:\\n{input}\\n\\n### "], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "= \"\" DEFAULT_UNK_TOKEN = \"\" PROMPT_DICT = { \"prompt_input\": ( \"Below is an instruction that describes a task, paired with an input that provides further context. \" \"Write a response that appropriately completes the request.\\n\\n\" \"### Instruction:\\n{instruction}\\n\\n### Input:\\n{input}\\n\\n### Response:\" ), \"prompt_no_input\": ( \"Below is an instruction that describes a task. \" \"Write a response that appropriately completes the request.\\n\\n\" \"### Instruction:\\n{instruction}\\n\\n### Response:\" ), } @dataclass class ModelArguments: model_name_or_path: Optional[str] = field(default=\"facebook/opt-125m\") @dataclass class DataArguments: data_path: str = field(default=None, metadata={\"help\": \"Path to the training data.\"}) @dataclass class TrainingArguments(transformers.TrainingArguments): cache_dir: Optional[str] = field(default=None) optim: str = field(default=\"adamw_torch\") model_max_length: int = field( default=512, metadata={\"help\": \"Maximum sequence length. Sequences will be right padded (and possibly truncated).\"}, ) def smart_tokenizer_and_embedding_resize( special_tokens_dict: Dict, tokenizer: transformers.PreTrainedTokenizer, model: transformers.PreTrainedModel, ): \"\"\"Resize tokenizer and embedding. Note: This is the unoptimized version that may make your embedding size not be divisible by 64. \"\"\" num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict) model.resize_token_embeddings(len(tokenizer)) if num_new_tokens > 0: input_embeddings = model.get_input_embeddings().weight.data output_embeddings = model.get_output_embeddings().weight.data input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) input_embeddings[-num_new_tokens:] = input_embeddings_avg output_embeddings[-num_new_tokens:] = output_embeddings_avg def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict: \"\"\"Tokenize a list of strings.\"\"\" tokenized_list = [ tokenizer( text, return_tensors=\"pt\", padding=\"longest\", max_length=tokenizer.model_max_length, truncation=True, ) for text in strings ] input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list] input_ids_lens = labels_lens = [ tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list ] return dict( input_ids=input_ids, labels=labels, input_ids_lens=input_ids_lens, labels_lens=labels_lens, ) def preprocess( sources: Sequence[str], targets: Sequence[str], tokenizer: transformers.PreTrainedTokenizer, ) -> Dict: \"\"\"Preprocess the data by tokenizing.\"\"\" examples = [s + t for s, t in zip(sources, targets)] examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer) for strings in (examples, sources)] input_ids = examples_tokenized[\"input_ids\"] labels = copy.deepcopy(input_ids) for label, source_len in zip(labels, sources_tokenized[\"input_ids_lens\"]): label[:source_len] = IGNORE_INDEX return dict(input_ids=", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_aa1ae58d9470", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.8, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Write a short story in third person narration about a protagonist who has to make an important career decision. -> Write a short story in third person narration about a protagonist who has to make an important career decision. -> John was at a crossroads in his life. He had just graduated college and was now facing the big decision of what career to pursue. After much deliberation, he decided that he wanted to b"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Write a short story in third person narration about a protagonist who has to make an important career decision. -> John was at a crossroads in his life. He had just graduated college and was now facing the big decision of what career to pursue. After much deliberation, he decided that he wanted to be an accountant and help the financially disadvantaged. He had always been good with numbers and enjoyed seeing the tangible results of his work. John enrolled in accounting courses and initially found it quite challenging. He had to learn multiple systems and regulations quickly, but he worked hard and eventually excelled in his studies. After a few years, John started working at an accounting firm in his city. He was eager to put his knowledge of taxes and accounting to use in a real-world setting. John loved his job, as it let him express his creativity in finding strategies to save his clients money. After a few years at the firm, he became a senior accountant and was asked to manage bigger and more challenging cases. He was now a respected figure in the financial industry, but he still remembers when he was just a recent college graduate, unsure of the direction in which his life would take him. ->", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_7409d0a69df5", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.8, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Discuss the causes of the Great Depression -> Discuss the causes of the Great Depression -> The Great Depression was a severe global economic downturn that lasted from 1929 to 1939. It was caused by several factors, including the stock market crash of 1929, structural weaknesses in the economy, low consumer spending, high unemployment, and a la"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Discuss the causes of the Great Depression -> The Great Depression was a severe global economic downturn that lasted from 1929 to 1939. It was caused by several factors, including the stock market crash of 1929, structural weaknesses in the economy, low consumer spending, high unemployment, and a lack of confidence in the banking system. Other contributing factors included overproduction of commodities, reliance on borrowing to finance investments, agricultural overproduction, and a decrease in international trade. ->", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_50664b81aee8", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.8, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["CULTURE_VIE Given the question: I am trying to decide whether it's worth it to invest in this film proposal. Can you help me answer a few questions? If you can't, please say \"No I can't\". Question: What is Tool' -> Given the question: I am trying to decide whether it's worth it to invest in this film proposal. Can you help me answer a few questions? If you can't, please say \"No I can't\". Question: What is Tool's relation to Ross. Movie title: The Expendables Movie plot: The movie is a Vietnam exploitation fil"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Given the question: I am trying to decide whether it's worth it to invest in this film proposal. Can you help me answer a few questions? If you can't, please say \"No I can't\". Question: What is Tool's relation to Ross. Movie title: The Expendables Movie plot: The movie is a Vietnam exploitation film where a commando makes an elite team out of rebels. The men are trouble makers and rebels, but are able to perform well. The men are sent out to blow up a bridge and manage to destroy the North Vietnamese Army (NVA) guards. However, another NVA force arrives and an all out battle commences. During the battle, two of the men are killed, but the team kills off all of the NVA reinforcements and captures their leader. The team is next sent on another mission to capture two village women, and then another to destroy an enemy base where another member is killed. They kill all of the NVA forces in the base, but their mission gets tougher when a prisoner escapes and captures American women and holds them hostage. The NVA splits up into two forces: one at an old fort and the other to ambush the team as it arrives. Despite the NVA plan of ambush, the NVA are wiped out, but they manage to kill two of the women and one of the special squad. Realizing that there is little time, the squad goes on with their mission and finds the fort. Their squad leader goes inside the fort to recon and is captured. The squad decides to go in and rescue him, the POW girls, and kill the NVA leader. The answer is: -> Tool is a character in The Expendables movie and is played by actor Mickey Rourke. Tool is a former member of the elite team of mercenaries known as the Expendables. He is now the team's tattoo artist and provides moral support. Tool is a friend and confidant of the team leader, Barney Ross, played by Sylvester Stallone. The two share a bond as they have both experienced the adrenaline, pain, and emotional toll of their dangerous line of work. In summary, Tool's relation to Ross is that he is a former teammate, friend, and confidant. -> You are an AI assistant. User will you give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_64603626cb4e", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.8, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Extract the owner of Lamborghini and a listing of the different types of Huracan cars that Lamborghini has produced for its Motorsport division. -> its first decade, but sales plunged in the wake of the 1973 worldwide financial downturn and the oil crisis. The firm's ownership changed three times after 1973, including a bankruptcy in 1978. American Chrysler Corporation took control of Lamborghini in 1987 and sold it to Malaysian investment grou"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "its first decade, but sales plunged in the wake of the 1973 worldwide financial downturn and the oil crisis. The firm's ownership changed three times after 1973, including a bankruptcy in 1978. American Chrysler Corporation took control of Lamborghini in 1987 and sold it to Malaysian investment group Mycom Setdco and Indonesian group V'Power Corporation in 1994. In 1998, Mycom Setdco and V'Power sold Lamborghini to the Volkswagen Group where it was placed under the control of the group's Audi division. New products and model lines were introduced to the brand's portfolio and brought to the market and saw an increased productivity for the brand. In the late 2000s, during the worldwide financial crisis and the subsequent economic crisis, Lamborghini's sales saw a drop of nearly 50 per cent. Lamborghini currently produces the V12-powered Aventador and the V10-powered Huracán, along with the Urus SUV powered by a twin-turbo V8 engine. In addition, the company produces V12 engines for offshore powerboat racing. Lamborghini Trattori, founded in 1948 by Ferruccio Lamborghini, is headquartered in Pieve di Cento, Italy and continues to produce tractors. Since 1973, Lamborghini Trattori has been a separate entity from the automobile manufacturer. History Main article: History of Lamborghini Ferruccio Lamborghini with a Jarama and a tractor of his brand Manufacturing magnate Italian Ferruccio Lamborghini founded the company in 1963 with the objective of producing a refined grand touring car to compete with offerings from established marques such as Ferrari. The company's first models, such as the 350 GT, were released in the mid-1960s. Lamborghini was noted for the 1966 Miura sports coupé, which used a rear mid-engine, rear-wheel drive layout.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_c1cf3d23614e", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.8, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE What is underwriting? -> What is underwriting? -> Underwriting (UW) services are provided by some large financial institutions, such as banks, insurance companies and investment houses, whereby they guarantee payment in case of damage or financial loss and accept the financial risk for liability arising from such guarantee."], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "What is underwriting? -> Underwriting (UW) services are provided by some large financial institutions, such as banks, insurance companies and investment houses, whereby they guarantee payment in case of damage or financial loss and accept the financial risk for liability arising from such guarantee. An underwriting arrangement may be created in a number of situations including insurance, issues of security in a public offering, and bank lending, among others. The person or institution that agrees to sell a minimum number of securities of the company for commission is called the underwriter. The term \"underwriting\" derives from the Lloyd's of London insurance market. Financial backers (or risk takers), who would accept some of the risk on a given venture (historically a sea voyage with associated risks of shipwreck) in exchange for a premium, would literally write their names under the risk information that was written on a Lloyd's slip created for this purpose. ->", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_c508844b309d", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.8, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE What is the currency in use in the Netherlands? -> What is the currency in use in the Netherlands? -> The currency in use in the Netherlands is the euro. ->"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "What is the currency in use in the Netherlands? -> The currency in use in the Netherlands is the euro. ->", "_quality": 0.7, "_expert": "expert_finance"}
{"id": "AEX_scraped_b4df59d1d06b", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.8, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE What is investment banking? -> What is investment banking? -> Investment banking pertains to certain activities of a financial services company or a corporate division that consist in advisory-based financial transactions on behalf of individuals, corporations, and governments. Traditionally associated with corporate finance, suc"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "What is investment banking? -> Investment banking pertains to certain activities of a financial services company or a corporate division that consist in advisory-based financial transactions on behalf of individuals, corporations, and governments. Traditionally associated with corporate finance, such a bank might assist in raising financial capital by underwriting or acting as the client's agent in the issuance of debt or equity securities. An investment bank may also assist companies involved in mergers and acquisitions (M&A) and provide ancillary services such as market making, trading of derivatives and equity securities, FICC services (fixed income instruments, currencies, and commodities) or research (macroeconomic, credit or equity research). Most investment banks maintain prime brokerage and asset management departments in conjunction with their investment research businesses. As an industry, it is broken up into the Bulge Bracket (upper tier), Middle Market (mid-level businesses), and boutique market (specialized businesses). Unlike commercial banks and retail banks, investment banks do not take deposits. ->", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_000a97aaa7dd", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.8, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE What many companies do when planning their global marketing strategies is to organize the world’s countries into three categories.\nEmerging Markets – Markets regarded as having the most potential for -> What many companies do when planning their global marketing strategies is to organize the world’s countries into three categories.\nEmerging Markets – Markets regarded as having the most potential for growth (e.g. huge countries such as China and India) and targeted for long-term investment.\nRest of "], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "What many companies do when planning their global marketing strategies is to organize the world’s countries into three categories. Emerging Markets – Markets regarded as having the most potential for growth (e.g. huge countries such as China and India) and targeted for long-term investment. Rest of the World (ROW) Markets – Markets that are often not included in a global marketing strategy because a company does not have sufficient time or resources to pay them any attention. A typical company involved in global marketing is likely to have four or five core markets and maybe six to twelve emerging markets. Is it Wise to Ignore the ROW? Collectively the ROW consists of a huge market of over a billion people. Of course the ROW includes a lot of small countries, but even a country with only five to six million people (e.g. Denmark and Ireland) could be very profitable for the right product. Click here for a list of countries in order of population. Which Languages Does the “Rest of the World” Speak? The Web Globalization Report Card, which tracks the websites of 150 global companies, reports a steadily increasing number of languages supported by global content. As of 2018, the average number of languages supported by all 150 global websites is now thirty-two. The success of global marketing can only be achieved through the use of local languages. If the growth in the number of languages is imagined as a curve, this curve begins quite steeply as companies load up on the core European and Asian languages. After a company reaches the level of twenty languages, the curve starts to taper off. The number of languages supported by a few companies, such as Facebook, Visa, and Dyson has shot up in sudden spikes. However, most companies are only in the early stages of this growth curve. It would be a herculean task to utilize the more than 6,000 languages spoken worldwide, so a global marketing company has to narrow down its language choices to the languages spoken by its core target audiences. The following is a list of ten languages that are shared by leading global websites. English – English is the language of globalization and is spoken by over 75% of the world’s population. Chinese (Simplified) – If people in Chinese speaking markets continue to expand their internet usage, Chinese might one day supplant English as the most widely used internet language. Arabic – The Arabic language is spoken by 295 million speakers worldwide, and is the official language of 28 different countries. A report from the British Council ranks Arabic as the second most important language of the future. Spanish – There are a number of important Spanish speaking countries, not to mention that the Hispanic population in the US will likely double by 2050. German – The German language is the fourth most spoken language in the world with nearly 210 million speakers worldwide. Portuguese – Second behind Spanish in Latin America. Portuguese is spoken by 215 million people in Portugal", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_d1587c8f3ea2", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.8, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Prepare an agenda and schedule a date and time for a meeting where the marketing team will discuss the goals, target audience, budget, strategies, and timeline for the implementation of the new market -> Prepare an agenda and schedule a date and time for a meeting where the marketing team will discuss the goals, target audience, budget, strategies, and timeline for the implementation of the new marketing campaign. Make sure to invite all relevant team members and prepare any necessary presentation m"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Prepare an agenda and schedule a date and time for a meeting where the marketing team will discuss the goals, target audience, budget, strategies, and timeline for the implementation of the new marketing campaign. Make sure to invite all relevant team members and prepare any necessary presentation materials ahead of time.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_4ba48beb5f49", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.8, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Could you explore in more detail how one would go about comparing financial performance side-by-side or exploring individual companies using Company REFS online? Answer according to: Do you need valua -> Could you explore in more detail how one would go about comparing financial performance side-by-side or exploring individual companies using Company REFS online? Answer according to: Do you need valuation data for a listed company? Company REFS online is now freely available to ICAEW members. You ca"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Could you explore in more detail how one would go about comparing financial performance side-by-side or exploring individual companies using Company REFS online? Answer according to: Do you need valuation data for a listed company? Company REFS online is now freely available to ICAEW members. You can compare financial performance side-by-side or explore individual companies in more detail.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_4e684abc9a2a", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.8, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Robert Humphreys has been appointed as a non-executive director at the Society of Independent Brewers (SIBA).\nHumphreys, formerly honorary secretary of the All Party Parliamentary Beer Group, will sit -> Robert Humphreys has been appointed as a non-executive director at the Society of Independent Brewers (SIBA).\nHumphreys, formerly honorary secretary of the All Party Parliamentary Beer Group, will sit on the SIBA Executive, created earlier this year as part of an organisational restructure which uni"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Robert Humphreys has been appointed as a non-executive director at the Society of Independent Brewers (SIBA). Humphreys, formerly honorary secretary of the All Party Parliamentary Beer Group, will sit on the SIBA Executive, created earlier this year as part of an organisational restructure which unified SIBA’s trade association and commercial arms. Humphreys says: “I have watched with great pleasure SIBA’s growth from a small band of founding microbrewers in 1980, to the influential and respected trade body it is today. While much has changed in those 30+ years, SIBA’s driving force is still its passion for beer and this, combined with its new structure, put it in prime position to represent and develop the British brewing industry. Besides Humphreys, Francis Patton and three elected directors, including SIBA’s chairman Guy Sheppard, serve on the SIBA Executive, alongside the senior management team comprising Benner, commercial director Nick Stafford and part-time finance director John Hart. A board of 24 trustees, drawn from SIBA’s brewer members, set strategy for and ratify decisions by the executive. Who has been appointed as a non-executive director at SIBA and what is their background in the beer industry?", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_982768a24628", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.8, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE How can scientists accurately identify and classify a newly discovered organism into the existing taxonomic system? -> How can scientists accurately identify and classify a newly discovered organism into the existing taxonomic system? -> To accurately identify and classify a newly discovered organism into the existing taxonomic system, scientists follow a series of steps and use various tools and techniques. Here's "], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "How can scientists accurately identify and classify a newly discovered organism into the existing taxonomic system? -> To accurately identify and classify a newly discovered organism into the existing taxonomic system, scientists follow a series of steps and use various tools and techniques. Here's an outline of the process: 1. Observation and documentation: The first step is to carefully observe and document the organism's physical characteristics, behavior, habitat, and other relevant information. High-quality photographs, videos, and detailed notes are essential for further analysis. 2. Morphological analysis: Scientists examine the organism's morphology (shape, size, and structure) and compare it with known organisms. This helps in determining whether the organism is a new species or a variant of an existing one. 3. Molecular analysis: DNA sequencing and other molecular techniques are used to analyze the organism's genetic material. This provides valuable information about the organism's evolutionary relationships and helps in determining its position in the taxonomic system. 4. Literature review: Scientists review existing scientific literature to gather information about similar organisms and their classification. This helps in understanding the organism's evolutionary history and its relationships with other species. 5. Consultation with experts: Scientists consult with taxonomic experts who specialize in the group of organisms to which the new discovery belongs. These experts can provide valuable insights and guidance on the classification of the organism. 6. Phylogenetic analysis: By comparing the organism's genetic material with that of other related species, scientists can construct a phylogenetic tree, which shows the evolutionary relationships among species. This helps in determining the organism's position in the taxonomic system and its relationships with other species.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_764cd094b9a8", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.8, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE What physical characteristics are used to classify organisms into different kingdoms, and how are these characteristics different from those used to classify organisms into other taxonomic categories -> plants (Kingdom Plantae), produce their own food through photosynthesis, while heterotrophs, like animals (Kingdom Animalia) and fungi (Kingdom Fungi), obtain their food by consuming other organisms. 4. Reproduction: The mode of reproduction is another characteristic used for classification. Some or"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "plants (Kingdom Plantae), produce their own food through photosynthesis, while heterotrophs, like animals (Kingdom Animalia) and fungi (Kingdom Fungi), obtain their food by consuming other organisms. 4. Reproduction: The mode of reproduction is another characteristic used for classification. Some organisms reproduce asexually (e.g., bacteria in Kingdom Monera), while others reproduce sexually (e.g., organisms in Kingdoms Plantae, Animalia, and Fungi). 5. Body organization: The level of body organization, such as the presence of tissues, organs, and organ systems, is used to classify organisms. For example, animals (Kingdom Animalia) have a higher level of body organization compared to other kingdoms. In contrast, the classification of organisms into lower taxonomic categories like genus and species is based on more specific and detailed characteristics. These characteristics often include: 1. Morphological features: The shape, size, and structure of various body parts are used to differentiate between species and genera. For example, the shape of a leaf or the arrangement of petals in a flower can be used to distinguish between plant species. 2. Genetic similarities: DNA sequences and other genetic markers are used to determine the relatedness between organisms. Species within the same genus typically have more genetic similarities compared to species from different genera. 3. Reproductive compatibility: Species are often defined by their ability to interbreed and produce fertile offspring. Organisms that cannot produce fertile offspring when crossed are considered separate species. 4. Ecological niche: The specific role an organism plays in its ecosystem, including its habitat, diet, and interactions with other organisms, can be used to differentiate between species and genera.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_6c03048555a3", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.8, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE What are the key physical characteristics used to classify organisms into different taxonomic groups? -> radial symmetry (symmetrical around a central axis), bilateral symmetry (symmetrical along a single plane), or asymmetry (no symmetry). 4. Body plan: The arrangement of body parts and tissues can be used to classify organisms, such as the presence of a coelom (a body cavity), segmentation (repeated "], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "radial symmetry (symmetrical around a central axis), bilateral symmetry (symmetrical along a single plane), or asymmetry (no symmetry). 4. Body plan: The arrangement of body parts and tissues can be used to classify organisms, such as the presence of a coelom (a body cavity), segmentation (repeated body units), or the type of skeleton (endoskeleton or exoskeleton). 5. Reproduction: Organisms can be classified based on their reproductive strategies, such as sexual reproduction (involving the fusion of gametes) or asexual reproduction (involving the production of offspring without the fusion of gametes). 6. Mode of nutrition: Organisms can be classified based on how they obtain their nutrients, such as autotrophs (organisms that produce their own food through photosynthesis or chemosynthesis) or heterotrophs (organisms that obtain nutrients by consuming other organisms). 7. Locomotion: The presence or absence of specific structures for movement, such as flagella, cilia, or limbs, can be used to classify organisms. 8. Morphological features: Specific physical features, such as the presence of feathers, scales, or fur, can be used to classify organisms into different taxonomic groups. 9. Biochemical and genetic characteristics: The analysis of DNA, RNA, and protein sequences can provide valuable information for classifying organisms and determining their evolutionary relationships. These physical characteristics, along with other features, are used to classify organisms into different taxonomic groups, such as domain, kingdom, phylum, class, order, family, genus, and species. This hierarchical system helps scientists to understand the relationships between organisms and their evolutionary history.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_56ece1d801b2", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.8, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE What physical characteristics are used to classify organisms into different taxonomic groups, and how do those characteristics vary among different organisms? -> wall made of chitin. 3. Phylum: Within each kingdom, organisms are further classified into phyla based on their body plan and other morphological features. For example, in the animal kingdom, organisms are classified into phyla such as Chordata (having a notochord), Arthropoda (having jointed append"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "wall made of chitin. 3. Phylum: Within each kingdom, organisms are further classified into phyla based on their body plan and other morphological features. For example, in the animal kingdom, organisms are classified into phyla such as Chordata (having a notochord), Arthropoda (having jointed appendages), and Mollusca (having a soft body and a muscular foot). 4. Class: Within each phylum, organisms are classified into classes based on more specific physical characteristics. For example, within the phylum Chordata, organisms are classified into classes such as Mammalia (having hair and mammary glands), Aves (having feathers and beaks), and Reptilia (having scales and laying eggs). 5. Order: Within each class, organisms are classified into orders based on even more specific characteristics. For example, within the class Mammalia, organisms are classified into orders such as Primates (having opposable thumbs and large brains), Carnivora (having sharp teeth and claws for hunting), and Cetacea (having a streamlined body and flippers for swimming). 6. Family: Within each order, organisms are classified into families based on shared physical traits. For example, within the order Primates, organisms are classified into families such as Hominidae (having a large brain and bipedal locomotion) and Cercopithecidae (having a tail and quadrupedal locomotion). 7. Genus and Species: Within each family, organisms are classified into genera and species based on even more specific physical characteristics and genetic similarities. For example, within the family Hominidae, organisms are classified into the genus Homo (having a large brain and a flat face) and species such as Homo sapiens (having a high forehead and a prominent chin).", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_56cbab2311fc", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.8, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE What physical characteristics are used in the classification of organisms, and how do those characteristics determine their placement in different taxa? Provide examples of organisms that are classifi -> What physical characteristics are used in the classification of organisms, and how do those characteristics determine their placement in different taxa? Provide examples of organisms that are classified using these physical characteristics. -> The classification of organisms is based on a hierarchic"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "What physical characteristics are used in the classification of organisms, and how do those characteristics determine their placement in different taxa? Provide examples of organisms that are classified using these physical characteristics. -> The classification of organisms is based on a hierarchical system called taxonomy, which was developed by Carl Linnaeus in the 18th century. Taxonomy classifies organisms into different groups or taxa based on their shared physical characteristics. These characteristics can include morphology (shape and structure), anatomy (internal structure), physiology (functions and processes), and molecular (genetic) traits. Here are some examples of physical characteristics used in the classification of organisms and how they determine their placement in different taxa: 1. Morphology: The overall shape and structure of an organism can be used to classify it into a particular taxon. For example, animals with a backbone are classified as vertebrates, while those without a backbone are classified as invertebrates. Similarly, plants with vascular tissues (xylem and phloem) are classified as vascular plants, while those without vascular tissues are classified as non-vascular plants. 2. Anatomy: The internal structure of an organism can also be used for classification. For example, animals with a three-chambered heart, such as amphibians and most reptiles, are classified differently from those with a four-chambered heart, such as mammals and birds. In plants, the presence or absence of specific structures like seeds, flowers, and leaves can determine their classification into groups like angiosperms (flowering plants), gymnosperms (cone-bearing plants), or bryophytes (mosses and liverworts).", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_3c8c43226455", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.8, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE What physical characteristics are used in the classification of organisms and how do these characteristics help to distinguish between different taxa? -> have specialized tissues and organs. 3. Mode of nutrition: Organisms can be classified as autotrophs (produce their own food through photosynthesis or chemosynthesis) or heterotrophs (consume other organisms for food). This characteristic helps distinguish between plants, animals, and fungi. 4. Repr"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "have specialized tissues and organs. 3. Mode of nutrition: Organisms can be classified as autotrophs (produce their own food through photosynthesis or chemosynthesis) or heterotrophs (consume other organisms for food). This characteristic helps distinguish between plants, animals, and fungi. 4. Reproduction: The mode of reproduction, such as sexual or asexual, and the presence of specialized reproductive structures can help differentiate between different groups of organisms. 5. Morphology: The overall body shape, size, and structure of an organism can be used to classify it into different taxa. For example, the presence of a backbone can distinguish between vertebrates and invertebrates. 6. Symmetry: The arrangement of body parts around a central axis can help classify organisms as radial (circular arrangement) or bilateral (mirror-image arrangement) symmetry. 7. Locomotion: The presence and type of structures used for movement, such as flagella, cilia, or limbs, can help differentiate between different groups of organisms. 8. Skeletal structure: The presence and type of internal or external skeletal structures, such as exoskeletons or endoskeletons, can be used to classify organisms. 9. Biochemistry: The types of proteins, enzymes, and other molecules present in an organism can provide clues about its evolutionary relationships and help classify it into different taxa. By examining these physical characteristics, biologists can group organisms into related taxa and better understand their evolutionary relationships. This classification system allows for a more organized and systematic study of the vast diversity of life on Earth.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_d26f2cb8ed9d", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Lex Fridman Podcast: More Trump Tariffs Are Coming -> Podcast: Lex Fridman Podcast\nEpisode: More Trump Tariffs Are Coming\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nAt a moment when inflation and affordability are on virtually everyone’s mind in America, Jamieson Greer, the U.S. trade representative, said the Trump administrati"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: Lex Fridman Podcast Episode: More Trump Tariffs Are Coming Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} At a moment when inflation and affordability are on virtually everyone’s mind in America, Jamieson Greer, the U.S. trade representative, said the Trump administration was planning a new slate of tariffs. Ana Swanson, who covers international trade for The New York Times and spoke to Mr. Greer, explains why Greer thinks the policy has been a success. Guest: Ana Swanson covers trade and international economics for The New York Times. Background reading: Mr. Greer is the quiet architect of President Trump’s global trade war. Photo: Tierney L. Cross/The New York Times For more information on today’s episode, visit nytimes.com/thedaily . Transcripts of each episode will be made available by the next workday. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher . For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_875dd87adc7e", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Lex Fridman Podcast: The Unprecedented Personal Profits of Trump’s Presidency -> Podcast: Lex Fridman Podcast\nEpisode: The Unprecedented Personal Profits of Trump’s Presidency\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nA recent financial disclosure shows that President Trump has made $2.2 billion since he returned to office. Today, Eric Lipton, a New Yor"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: Lex Fridman Podcast Episode: The Unprecedented Personal Profits of Trump’s Presidency Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} A recent financial disclosure shows that President Trump has made $2.2 billion since he returned to office. Today, Eric Lipton, a New York Times investigative reporter, reveals the Trump family’s next potential windfall and how the president’s position is once again helping it happen. Guest: Eric Lipton , an investigative reporter for The New York Times. Background reading: Mr. Trump and his family reaped huge financial rewards from a crypto venture , even as routine investors suffered vast losses. He has collected about $1.4 billion in new revenue from businesses that directly benefited from his actions as president. Photo: Sergey Ponomarev for The New York Times For more information on today’s episode, visit nytimes.com/thedaily . Transcripts of each episode will be made available by the next workday. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher . For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_9db74fc424ff", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Lex Fridman Podcast: Congressional Republicans Try a New Approach: Telling Trump No -> Podcast: Lex Fridman Podcast\nEpisode: Congressional Republicans Try a New Approach: Telling Trump No\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nFrom the war in Iran to his plan to use taxpayer money to pay his allies, the Republican-controlled Congress has begun rebelling ag"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: Lex Fridman Podcast Episode: Congressional Republicans Try a New Approach: Telling Trump No Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} From the war in Iran to his plan to use taxpayer money to pay his allies, the Republican-controlled Congress has begun rebelling against President Trump. Today, Julie Hirschfeld Davis, a congressional editor at The New York Times, discusses whether this rebellion is a preview of a new dynamic in Washington, or a temporary show of independence that will vanish just as quickly as it arrived. Guest: Julie Hirschfield Davis , congressional editor at The New York Times. Background reading: Mr. Trump says he never promised no new wars and defends the compensation fund. Photo: Haiyun Jiang/The New York Times For more information on today’s episode, visit nytimes.com/thedaily . Transcripts of each episode will be made available by the next workday. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher . For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_8e5cf3dbad1e", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Lex Fridman Podcast: How Elon Musk Engineered the World’s Biggest I.P.O. -> Podcast: Lex Fridman Podcast\nEpisode: How Elon Musk Engineered the World’s Biggest I.P.O.\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nSpaceX, Elon Musk’s rocket and satellite maker, is set to go public and begin selling shares as soon as next week. Ryan Mac, who reports on bu"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: Lex Fridman Podcast Episode: How Elon Musk Engineered the World’s Biggest I.P.O. Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} SpaceX, Elon Musk’s rocket and satellite maker, is set to go public and begin selling shares as soon as next week. Ryan Mac, who reports on business, explains the plan for the company’s expected record-shattering debut on the stock market, and how it is changing the rules for investing. Guest: Ryan Mac , a New York Times reporter based in Los Angeles who covers corporate accountability across the global technology industry. Background reading: Why sky-high I.P.O. pricing isn’t great for real people. From April: SpaceX filed to go public , setting the stage for a huge I.P.O. Photo: Steve Nesius/Reuters For more information on today’s episode, visit nytimes.com/thedaily . Transcripts of each episode will be made available by the next workday. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher . For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_c72d188870b7", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Lex Fridman Podcast: A New Leader — and a New Showdown — at the Fed -> Podcast: Lex Fridman Podcast\nEpisode: A New Leader — and a New Showdown — at the Fed\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nAfter a year of harassing and threatening Jerome Powell, the chairman of the Federal Reserve, President Trump replaced him on Wednesday. Colby Smit"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: Lex Fridman Podcast Episode: A New Leader — and a New Showdown — at the Fed Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} After a year of harassing and threatening Jerome Powell, the chairman of the Federal Reserve, President Trump replaced him on Wednesday. Colby Smith, who covers the Fed, explains how the president ended one standoff only to create a new one. Guest: Colby Smith , a New York Times reporter covering the Federal Reserve and the U.S. economy. Background reading: The Senate confirmed Kevin Warsh as the new chair of the Federal Reserve. Video: How Jerome Powell managed a chaotic era . Photo: Kenny Holston/The New York Times, Caroline Gutman for The New York Times For more information on today’s episode, visit nytimes.com/thedaily . Transcripts of each episode will be made available by the next workday. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher . For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_63053b5cc40b", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Lex Fridman Podcast: Two Superpowers Across the Table -> Podcast: Lex Fridman Podcast\nEpisode: Two Superpowers Across the Table\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nHere’s what to expect from the summit between President Trump and China’s top leader, Xi Jinping. For the first time in nearly a decade, President Trump will mee"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: Lex Fridman Podcast Episode: Two Superpowers Across the Table Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} Here’s what to expect from the summit between President Trump and China’s top leader, Xi Jinping. For the first time in nearly a decade, President Trump will meet with President Xi Jinping of China in Beijing, where they are expected to discuss trade, technology and other points of contention. David E. Sanger, who covers the Trump administration for The New York Times, explains what is likely to come from the meeting. Guest: David E. Sanger , a White House and national security correspondent for The New York Times. Background reading: Here’s what to know about the meeting. Video: How Mr. Trump has changed the way he talks about China. Photo: Haiyun Jiang/The New York Times For more information on today’s episode, visit nytimes.com/thedaily . Transcripts of each episode will be made available by the next workday. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher . For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_5ff9a534dfa2", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Lex Fridman Podcast: Why Even Some Democrats Hate California’s Billionaire Tax Proposal -> Podcast: Lex Fridman Podcast\nEpisode: Why Even Some Democrats Hate California’s Billionaire Tax Proposal\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nA landmark proposal for a one-time tax on billionaires in California recently reached a milestone. The labor union backing it s"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: Lex Fridman Podcast Episode: Why Even Some Democrats Hate California’s Billionaire Tax Proposal Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} A landmark proposal for a one-time tax on billionaires in California recently reached a milestone. The labor union backing it said it had collected enough signatures to put the measure on the state’s ballot. Laurel Rosenhall, who covers California politics for The New York Times, explains how the state arrived at this moment and what it might mean nationwide. Guest: There are enough signatures to place the California billionaire tax proposal on the state ballot in November, its backers say. Background reading: There are enough signatures to place the California billionaire tax proposal on the state ballot in November, its backers say. Photo: Jae C. Hong/Associated Press For more information on today’s episode, visit nytimes.com/thedaily . Transcripts of each episode will be made available by the next workday. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher . For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_3baf50c14eee", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Lex Fridman Podcast: How China Made Itself Tariff-Proof -> Podcast: Lex Fridman Podcast\nEpisode: How China Made Itself Tariff-Proof\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nAbout a year into President Trump’s global trade war, China hasn’t just survived. It has emerged stronger than ever on the world stage. Keith Bradsher, the Bei"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: Lex Fridman Podcast Episode: How China Made Itself Tariff-Proof Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} About a year into President Trump’s global trade war, China hasn’t just survived. It has emerged stronger than ever on the world stage. Keith Bradsher, the Beijing bureau chief for The New York Times, discusses the domination of China’s robot-powered superfactories and how the country essentially made itself tariff-proof. Guest: Keith Bradsher , the Beijing bureau chief for The New York Times. Background reading: China’s secret weapon in the trade war is an army of factory robots . Beijing announced a record trade surplus in January as its exports flooded world markets. Photo: Qilai Shen for The New York Times For more information on today’s episode, visit nytimes.com/thedaily . Transcripts of each episode will be made available by the next workday. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher . For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_fbfd05221f70", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Lex Fridman Podcast: What We’ve Learned From 10 Days of War -> Podcast: Lex Fridman Podcast\nEpisode: What We’ve Learned From 10 Days of War\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nWhat began as a relentless U.S.-Israeli military assault on Iran has turned into a wider crisis as the disruption of the world’s oil markets spreads beyond"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: Lex Fridman Podcast Episode: What We’ve Learned From 10 Days of War Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} What began as a relentless U.S.-Israeli military assault on Iran has turned into a wider crisis as the disruption of the world’s oil markets spreads beyond the Middle East. Eric Schmitt, a national security correspondent for The New York Times, discusses what we know about the players involved in the fighting. Guest: Eric Schmitt , a national security correspondent for The New York Times based in Washington. Background reading: War with Iran has become the world’s latest economic hazard . Follow updates on the fighting in the Middle East. Photo: Arash Khamooshi for The New York Times For more information on today’s episode, visit nytimes.com/thedaily . Transcripts of each episode will be made available by the next workday. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher . For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_2dfde40f8b15", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Lex Fridman Podcast: How to Bet on (Literally) Anything -> Podcast: Lex Fridman Podcast\nEpisode: How to Bet on (Literally) Anything\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nThe explosion of prediction markets in the United States, where people bet on everything from Taylor Swift’s wedding date to election outcomes, has radically e"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: Lex Fridman Podcast Episode: How to Bet on (Literally) Anything Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} The explosion of prediction markets in the United States, where people bet on everything from Taylor Swift’s wedding date to election outcomes, has radically expanded the universe of gambling. David Yaffe-Bellany, a technology reporter, explains how these platforms became so widespread so fast, and how betting on everything could change the way we live. Guest: David Yaffe-Bellany , a technology reporter for The New York Times, covering the crypto industry. Background reading: David Yaffe-Bellany , a technology reporter for The New York Times, covering the crypto industry. Photo: Photo Illustration: atakan/iStock, via Getty Images For more information on today’s episode, visit nytimes.com/thedaily . Transcripts of each episode will be made available by the next workday. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher . For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_052cb113a9c5", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Lex Fridman Podcast: Trump 2.0: The President’s Affordability Problem -> Podcast: Lex Fridman Podcast\nEpisode: Trump 2.0: The President’s Affordability Problem\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nPresident Trump was elected in 2024 on the promise that he would fix the economy. Now, a new poll from The New York Times/Siena reveals that the "], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: Lex Fridman Podcast Episode: Trump 2.0: The President’s Affordability Problem Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} President Trump was elected in 2024 on the promise that he would fix the economy. Now, a new poll from The New York Times/Siena reveals that the issue may be driving voters away. Nate Cohn, the chief political analyst at The Times, explains what the poll tells us. Guest: Nate Cohn , the chief political analyst for The New York Times. Background reading: Voters see a middle-class lifestyle as drifting out of reach , the poll found. Here’s what Americans really mean by “affordability.” Who are the voters who have taken a U-turn on Mr. Trump? Photo: Doug Mills/The New York Times For more information on today’s episode, visit nytimes.com/thedaily . Transcripts of each episode will be made available by the next workday. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher . For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_04aa20d986cd", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Lex Fridman: #477 – Keyu Jin: China’s Economy, Tariffs, Trade, Trump, Communism & Capitalism -> Podcast: Lex Fridman\nEpisode: #477 – Keyu Jin: China’s Economy, Tariffs, Trade, Trump, Communism & Capitalism\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: Lex Fridman Episode: #477 – Keyu Jin: China’s Economy, Tariffs, Trade, Trump, Communism & Capitalism Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}", "_quality": 0.6, "_expert": "expert_finance"}
{"id": "AEX_scraped_8af6fe3fe9c0", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Lex Fridman: #466 – Jeffrey Wasserstrom: China, Xi Jinping, Trade War, Taiwan, Hong Kong, Mao -> Podcast: Lex Fridman\nEpisode: #466 – Jeffrey Wasserstrom: China, Xi Jinping, Trade War, Taiwan, Hong Kong, Mao\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: Lex Fridman Episode: #466 – Jeffrey Wasserstrom: China, Xi Jinping, Trade War, Taiwan, Hong Kong, Mao Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}", "_quality": 0.7, "_expert": "expert_finance"}
{"id": "AEX_scraped_06f298ec093d", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["IA Lex Fridman: #453 – Javier Milei: President of Argentina – Freedom, Economics, and Corruption -> Podcast: Lex Fridman Episode: #453 – Javier Milei: President of Argentina – Freedom, Economics, and Corruption Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nJavier Milei is the President of Argentina. This episode is available in both English and Spanish. Thank you for listenin"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: Lex Fridman Episode: #453 – Javier Milei: President of Argentina – Freedom, Economics, and Corruption Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} Javier Milei is the President of Argentina. This episode is available in both English and Spanish. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep453-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/javier-milei-transcript CONTACT LEX: Feedback – give feedback to Lex: https://lexfridman.com/survey AMA – submit questions, videos or call-in: https://lexfridman.com/ama Hiring – join our team: https://lexfridman.com/hiring Other – other ways to get in touch: https://lexfridman.com/contact EPISODE LINKS: Javier Milei’s X: https://x.com/JMilei Javier Milei’s Instagram: https://instagram.com/javiermilei Javier Milei’s Facebook: https://facebook.com/JavierMileiEconomista SPONSORS: To support this podcast, check out our sponsors & get discounts: Eight Sleep: Temp-controlled smart mattress. Go to https://eightsleep.com/lex NetSuite: Business management software. Go to http://netsuite.com/lex BetterHelp: Online therapy and counseling. Go to https://betterhelp.com/lex AG1: All-in-one daily nutrition drinks. Go to https://drinkag1.com/lex LMNT: Zero-sugar electrolyte drink mix. Go to https://drinkLMNT.com/lex OUTLINE: (00:00) – Introduction (14:44) – Economic freedom (20:09) – Anarcho-capitalism (30:02) – Presidency and reforms (49:22) – Poverty (55:54) – Corruption (1:04:32) – Freedom (1:18:43) – Elon Musk (1:24:11) – DOGE (1:26:13) – Donald Trump (1:32:13) – US and Argentina relations (1:39:22) – Messi vs Maradona (1:48:16) – God (1:50:22) – Elvis and Rolling Stones (1:54:02) – Free market (2:01:03) – Loyalty (2:03:40) – Advice for young people (2:05:06) – Hope for Argentina PODCAST LINKS: – Podcast Website: https://lexfridman.com/podcast – Apple Podcasts: https://apple.co/2lwqZIr – Spotify: https://spoti.fi/2nEwCF8 – RSS: https://lexfridman.com/feed/podcast/ – Podcast Playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 – Clips Channel: https://www.youtube.com/lexclips SOCIAL LINKS: – X: https://x.com/lexfridman – Instagram: https://instagram.com/lexfridman – TikTok: https://tiktok.com/@lexfridman – LinkedIn: https://linkedin.com/in/lexfridman – Facebook: https://facebook.com/lexfridman – Patreon: https://patreon.com/lexfridman – Telegram: https://t.me/lexfridman – Reddit: https://reddit.com/r/lexfridman", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_3f4f5d85dcd1", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Lex Fridman: #413 – Bill Ackman: Investing, Financial Battles, Harvard, DEI, X & Free Speech -> Podcast: Lex Fridman\nEpisode: #413 – Bill Ackman: Investing, Financial Battles, Harvard, DEI, X & Free Speech\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: Lex Fridman Episode: #413 – Bill Ackman: Investing, Financial Battles, Harvard, DEI, X & Free Speech Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}", "_quality": 0.7, "_expert": "expert_finance"}
{"id": "AEX_scraped_ff95a7320dae", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE Lex Fridman: #345 – Coffeezilla: SBF, FTX, Fraud, Scams, Fake Gurus, Money, Fame, and Power -> Podcast: Lex Fridman Episode: #345 – Coffeezilla: SBF, FTX, Fraud, Scams, Fake Gurus, Money, Fame, and Power Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nCoffeezilla is a journalist and investigator on YouTube who exposes financial frauds, scams, and fake gurus. Please support"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: Lex Fridman Episode: #345 – Coffeezilla: SBF, FTX, Fraud, Scams, Fake Gurus, Money, Fame, and Power Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} Coffeezilla is a journalist and investigator on YouTube who exposes financial frauds, scams, and fake gurus. Please support this podcast by checking out our sponsors: – Rocket Money : https://rocketmoney.com/lex – Eight Sleep : https://www.eightsleep.com/lex to get special savings – BetterHelp : https://betterhelp.com/lex to get 10% off – MasterClass : https://masterclass.com/lex to get 15% off EPISODE LINKS: Coffeezilla’s YouTube: https://www.youtube.com/Coffeezilla Coffeezilla’s Twitter: https://twitter.com/coffeebreak_YT Coffeezilla’s Instagram: https://www.instagram.com/coffeebreak_yt PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/lexfridman – Twitter: https://twitter.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/lexfridman – Medium: https://medium.com/@lexfridman OUTLINE: Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) – Introduction (06:04) – Coffee (08:13) – SBF and FTX (22:54) – $8 billion (32:01) – Evil vs incompetence (41:57) – Key lessons from FTX collapse (55:15) – Should SBF go to jail? (1:03:02) – Role of influencers and celebrities (1:07:30) – How FTX covered up fraud (1:11:26) – Interview with SBF (1:26:29) – SafeMoon fraud (1:32:41) – Bitcoin (1:43:20) – Psychology of investigating fraud (1:52:26) – Investigating politics and corruption (1:59:39) – Coffeezilla origin story (2:04:19) – MLM marketing scams (2:11:58) – Andrew Tate and Hustlers University (2:29:40) – Save the Kids crypto scandal (2:36:59) – Money and fame (2:44:03) – MrBeast (2:50:26) – Fake gurus and Get-Rich-Quick schemes (3:09:45) – Process of investigation (3:22:31) – Twitter Files release (3:31:36) – Time management and productivity (3:43:36) – Advice for young people", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_d1837e6c9715", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE The Vergecast: Apple's plot to crush OpenAI -> Podcast: The Vergecast Episode: Apple's plot to crush OpenAI Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nIt's public beta season, which means David and Nilay have been upgrading some devices this week. We talk through the good and bad of our early experiences with Siri AI, an"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: The Vergecast Episode: Apple's plot to crush OpenAI Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} It's public beta season, which means David and Nilay have been upgrading some devices this week. We talk through the good and bad of our early experiences with Siri AI, and what it'll take for next-gen Siri to be a hit. After that, we discuss the real reason Apple decided to sue OpenAI over trade secrets, OpenAI's forthcoming smart speaker, the new Pixel colors, our emoji strategies, Brendan Carr, and much more. Further reading: Apple’s public betas for iOS 27 and more are out now Siri AI makes the Apple Watch finally feel like a wrist computer Siri AI is already changing how I use my iPhone The macOS 27 public beta is worth it just for the Liquid Glass tweaks Apple sues OpenAI for allegedly stealing hardware secrets Suno snatched millions of songs from YouTube, Genius, and Deezer Sam Altman didn’t need another lawsuit The 6 wildest claims in Apple’s lawsuit against OpenAI Another turn in the Apple v. OpenAI dispute. OpenAI has a new statement about Apple’s lawsuit. OnePlus officially gives up on the US and Europe OnePlus is dead in the US. Did it ever have a chance? Samsung shows off ‘brand new shape’ for Z Fold 8 in Spider-Man teaser OpenAI may announce a ChatGPT smart speaker this year OpenAI finally launches hardware... for Codex Pixel Watch 5 leak shows off four different finishes The Pixel colors might rule this year The PS6 sure sounds like a handheld Brendan Carr plans to let broadcast giants dominate the airwaves X admits its broken algorithm made the site feel like a ‘battleground’ States make last-ditch effort to stop the Paramount ‘media behemoth’ Paramount lead trial counsel on state AG suit: This merger is pro-competitive The new cracking face emoji might be an all-timer. Google’s Demis Hassabis says it’s time for a global AI watchdog — led by the US Google and Epic give up fighting — third-party Android app stores are coming next week Subscribe to The Verge for unlimited access to theverge.com , subscriber-exclusive newsletters, and our ad-free podcast feed .We love hearing from you! Email your questions and thoughts to vergecast@theverge.com or call us at 866-VERGE11. (Timestamps are approximate.) 00:01:00 Intro 00:02:00 Apple Betas and New Siri 00:08:00 Gemini Car Assistant Fails 00:15:00 Apple Sues OpenAI 00:18:00 Trade Secret Law Explained 00:28:00 Settlement or Long Fight 00:35:00 OpenAI Home Device Doubts 00:42:00 Smartphone Platform Lessons 00:50:00 Pixel Colors and Naming 00:52:00 OnePlus Retreat Explained 00:55:00 Carriers and iPhone Lock In 01:05:00 Brendan Carr Bribery Scandal 01:14:00 Emoji Lightning Round 01:18:00 X Algorithm Rediscovery 01:23:00 Foothills Of Singularity 01:29:00 Paramount Merger Spin 01:33:00 Wrap And Plugs Learn more about your ad choices. Visit podcastchoices.com/adchoices", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_908c46286500", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE The Vergecast: Of course Meta thinks gambling is the future -> Podcast: The Vergecast Episode: Of course Meta thinks gambling is the future Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nMeta's business is doing just fine. But Meta as a company, and Meta as a series of products? That is, uh, messier. David and Nilay discuss the company's on"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: The Vergecast Episode: Of course Meta thinks gambling is the future Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} Meta's business is doing just fine. But Meta as a company, and Meta as a series of products? That is, uh, messier. David and Nilay discuss the company's ongoing desire to be relevant and cool, the unceasing importance of Instagram, and why it makes perfect sense that Facebook would clone Polymarket. After that, the hosts talk about Apple's huge price increases, and the ways in which RAMageddon might change the gadget market forever. Then it's time for Brendan Carr is a dummy, the latest on the movie Artificial, and the looming fight over AI data. Further reading: The Steam Machine is the most ambitious game console I’ve ever played Valve prices the Steam Machine at $1,049 How much would the Steam Machine cost to build? Valve describes just how brutal RAM negotiations are in 2026 The Steam Machine is the start of an even more expensive future for game consoles I drove the Slate Truck — there’s more to it than EV minimalism The Slate Auto pickup truck starts at $24,950 Meta pauses employee tracking tool after internal leak. Now Meta will track what employees do on their computers to train its AI agents Meta CTO Andrew Bosworth Admits the Company’s AI Reorg Was ‘Atrocious’ | WIRED Zuckerberg reportedly wants a Polymarket clone — but without real money Polymarket paid creators to post fake videos of themselves placing and winning bets. Meta plans to release AI-powered prediction market app Facebook’s Creator Studio has been revived as an AI companion app Kaleidescape’s Strato E player blows streaming, and your wallet, away Something’s off with Midjourney’s pivot to body scanners People Inc. CEO says it’s “probably” headed for a confrontation with Google over AI crawling. ABC encourages viewers to back network amid FCC investigations Bob Iger’s Disney wanted Apple, Twitter, and 007 The film about Sam Altman has been dropped by Amazon MGM Subscribe to The Verge for unlimited access to theverge.com , subscriber-exclusive newsletters, and our ad-free podcast feed . We love hearing from you! Email your questions and thoughts to vergecast@theverge.com or call us at 866-VERGE11. (Timestamps are approximate.) 00:01:00 Cannes Cold Open 00:07:00 Coach x Spotify Absurdity 00:10:00 Vox Media PMX Shakeup 00:14:00 Meta Chaos vs Money 00:26:00 Gambling as Engagement 00:33:00 Ramageddon Hits Gadgets 00:44:00 Slate Truck Price 00:45:00 Range And Truck Feel 00:48:00 Tech Bloat Backlash 00:50:00 BYD Versus Tesla 00:56:00 FCC Targets The View 01:04:00 Amazon Drops Artificial 01:08:00 Kaleidescape Versus Blu Ray 01:13:00 Bob Iger Merger Rumors 01:17:00 Blocking AI Crawlers 01:22:00 Wrap Up And Next Week Learn more about your ad choices. Visit podcastchoices.com/adchoices", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_25f34e41a6ba", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE The Vergecast: How clips ate the internet -> Podcast: The Vergecast Episode: How clips ate the internet Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nIt's now surprisingly easy to watch most of a movie without ever trying to, or to spend hours with a podcast without ever playing an episode. In the burgeoning clip economy,"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: The Vergecast Episode: How clips ate the internet Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} It's now surprisingly easy to watch most of a movie without ever trying to, or to spend hours with a podcast without ever playing an episode. In the burgeoning clip economy, everything is being cut into bite-sized pieces and being blasted around the internet hoping to land in your feeds. The Verge's Mia Sato explains the machinery of how all this works, and wonders what it means for our social media experience. After that, The Verge's Victoria Song joins to discuss the Fitbit Air, the new $99 Google fitness tracker she and David have both been testing. It's a fascinating, thoroughly AI-ified device, and it actually has some pretty good ideas. (And some bad ones!) Finally, Vee sticks around to help David answer a question from the Vergecast Hotline (call 866-VERGE11 or email vergecast@theverge.com !) about smart glasses, and whether helping you find your other gear might just be a killer app. Further reading: Inside the cutthroat community of ‘clippers’ Google’s taking a big swing at AI health with the Fitbit Air What’s the role of a simple fitness band in the AI health era? All these smart glasses and nothing to do Subscribe to The Verge for unlimited access to theverge.com , subscriber-exclusive newsletters, and our ad-free podcast feed .We love hearing from you! Email your questions and thoughts to vergecast@theverge.com or call us at 866-VERGE11. (Timestamps are approximate.) 00:00:00 Brick Your Phone 00:03:00 Clips Go Industrial 00:06:00 How Clipping Platforms Work 00:08:00 Why It Looks Organic 00:11:00 Clavicular Case Study 00:13:00 Shady or Just Marketing 00:20:00 Platform Rules and Reality 00:26:00 Slop and the Future of Clips 00:36:00 Watch Band Color Debate 00:38:00 Why Fitbit Air Matters 00:40:00 Whoop Dupe Or Fitbit Roots 00:45:00 Google Health AI Coach 00:50:00 Limits And Lab Upload Friction 00:53:00 Privacy And Data Tradeoffs 00:56:00 AI Health Personalities Compared 01:04:00 Hotline Smart Glasses Tracking 01:09:00 Future Of All Day Glasses 01:13:00 Wrap Up Learn more about your ad choices. Visit podcastchoices.com/adchoices", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_3b085d78a080", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE The Vergecast: Ben McKenzie vs. crypto -> Podcast: The Vergecast Episode: Ben McKenzie vs. crypto Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nDuring the height of the cryptocurrency craze a few years ago, the actor Ben McKenzie found himself wondering why no one else was seeing what he was seeing. He joins the show t"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: The Vergecast Episode: Ben McKenzie vs. crypto Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} During the height of the cryptocurrency craze a few years ago, the actor Ben McKenzie found himself wondering why no one else was seeing what he was seeing. He joins the show to explain his yearslong attempt to understand Bitcoin and the crypto world, all of which is in his new documentary Everyone Is Lying to You For Money. (You can probably guess how he feels.) After that, The Verge's Victoria Song tells us about her testing of continuous glucose monitors, which have gone from medical device to influencer trend in some worrying ways. Finally, we answer a question from the Vergecast Hotline (call 866-VERGE11 or email vergecast@theverge.com !) about why all our gadgets seem the same these days. Vote for The Vergecast in the Webby Awards! A vote for The Vergecast is a vote that Brendan Carr is a dummy, that buttons are good, and that party speakers rule the world. Voting is open until April 16. https://vote.webbyawards.com/PublicVoting#/2026/podcasts/shows/technology Further reading: Everyone is Lying to You for Money Low interest rates and loneliness: the origins of the pandemic crypto boom Continuous glucose monitoring made me continuously crazy Subscribe to The Verge for unlimited access to theverge.com , subscriber-exclusive newsletters, and our ad-free podcast feed .We love hearing from you! Email your questions and thoughts to vergecast@theverge.com or call us at 866-VERGE11. (Timestamps are approximate.) 00:00:00 Sprinkler Duty Intro 00:03:00 Ben McKenzie Crypto Journey 00:04:00 Is Crypto Really Money 00:09:00 El Salvador Reality Check 00:11:00 Could Crypto Ever Work 00:14:00 Crypto Culture 00:19:00 Casino Capitalism And Crime 00:23:00 Why Bitcoin Keeps Rising 00:30:00 CGMs Explained 00:32:00 FDA OTC And Wellness Boom 00:33:00 Government Push For Wearables 00:39:00 Longevity Wearables Boom 00:41:00 Why Try CGMs 00:44:00 Scary Readings Doctor Visit 00:47:00 Living in the Data 00:48:00 Apps Scores Calibration 00:51:00 Disordered Eating Spiral 00:54:00 No Consensus for Non Diabetics 00:57:00 Medication Tradeoffs 01:00:00 Wellness vs Medical Regulation 01:04:00 Using CGMs Thoughtfully 01:10:00 Vergecast hotline 01:11:00 Why Gadgets Got Bland 01:22:00 Final Thoughts Learn more about your ad choices. Visit podcastchoices.com/adchoices", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_fe85f567dcc9", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE The Vergecast: The 6G, modular, robot phones of the future -> Podcast: The Vergecast\nEpisode: The 6G, modular, robot phones of the future\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nMost mainstream phone options are kind of the same, year in and year out — but that doesn’t mean there’s no innovation to be found. The Verge’s Allison John"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: The Vergecast Episode: The 6G, modular, robot phones of the future Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} Most mainstream phone options are kind of the same, year in and year out — but that doesn’t mean there’s no innovation to be found. The Verge’s Allison Johnson is at Mobile World Congress, and joins the show to report on all the modular phones, robot phones, small phones, big phones, and (alas) 6G phones set to hit the market this year. After that, The Verge’s Jess Weatherbed explains the phenomenon of the gadget strap, and makes the case that they’re an increasingly useful accessory as our phones become even more important to our daily lives. (Yes, even if you have pockets.) Finally, The Verge’s Jay Peters helps David answer a question from the Vergecast Hotline (call 866-VERGE11 or email vergecast@theverge.com !) about whether the metaverse, however you want to define it, is ever going to be realized. Further reading: Oh great, here comes 6G Honor claims its Robot Phone will launch later this year Lenovo made a Franken-laptop with modular ports and a second screen Vivo’s next phone will launch with a professional camera rig Tecno’s latest concept phone is lit by neon Honor’s Magic V6 is the first foldable with an IP69 rating The Motorola Razr Fold is shaping up to be pure flagship Xiaomi’s super-slim power bank costs extra in orange. Honor’s thinnest tablet doesn’t come cheap. Peak Design has wearable gadget straps for people who hate bags Apple’s misunderstood crossbody iPhone strap might be the best I’ve seen Meta confirms Reality Labs layoffs and shifts to invest more in wearables Meta’s VR metaverse is ditching VR Subscribe to The Verge for unlimited access to theverge.com , subscriber-exclusive newsletters, and our ad-free podcast feed .We love hearing from you! Email your questions and thoughts to vergecast@theverge.com or call us at 866-VERGE11. Learn more about your ad choices. Visit podcastchoices.com/adchoices", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_a43070b80f80", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE The Vergecast: Everything is gambling now -> Podcast: The Vergecast\nEpisode: Everything is gambling now\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nWho's going to win the Super Bowl? What about the latest season of Survivor? Or the race to be the next chair of the Federal Reserve? Who will be Portugal's next president? "], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: The Vergecast Episode: Everything is gambling now Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} Who's going to win the Super Bowl? What about the latest season of Survivor? Or the race to be the next chair of the Federal Reserve? Who will be Portugal's next president? How many times will Elon Musk tweet in the next week? On Polymarket, and other prediction markets, you can bet on all these things and more. Are we entering a world in which everything is gambling and gambling is everything? Bloomberg's Joe Weisenthal joins the show to explain the rise of prediction markets, what's betting and what's investing, and more. Then, The Verge's Hayden Field teaches us about Model Context Protocol, a wonky bit of AI infrastructure that might be key to making AI agents work. MCP is barely a year old, and practically all of tech is ready to embrace it. Finally, Hayden helps David answer a question on the Vergecast Hotline (call 866-VERGE11 or email vergecast@theverge.com !) about why every AI company seems to want you to go shopping. Further reading: Are prediction markets gambling? Robinhood CEO Vlad Tenev is betting not Election night at Kalshi HQ Joe Weisenthal at Bloomberg From Bloomberg : My Biggest Question About Prediction Markets Anthropic launches tool to connect AI systems directly to datasets AI companies want a new internet — and they think they’ve found the key Subscribe to The Verge for unlimited access to theverge.com , subscriber-exclusive newsletters, and our ad-free podcast feed .We love hearing from you! Email your questions and thoughts to vergecast@theverge.com or call us at 866-VERGE11. Learn more about your ad choices. Visit podcastchoices.com/adchoices", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_5916ba4c9aee", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE The Vergecast: 2025 year in review -> Podcast: The Vergecast\nEpisode: 2025 year in review\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nWell, friends, it's been a year. And before we turn the page to 2026 and all the stories of 2025 begin to blur together, we decided to take stock of things. Nilay and David are joi"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: The Vergecast Episode: 2025 year in review Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} Well, friends, it's been a year. And before we turn the page to 2026 and all the stories of 2025 begin to blur together, we decided to take stock of things. Nilay and David are joined by Wall Street Journal senior tech columnist Joanna Stern to debate the best products of the year, the biggest policy moves, the people who broke bad, the good AI things, the bad AI things, and much more. It's been a vibe-everything kind of year, and there's a lot to discuss. Subscribe to The Verge for unlimited access to theverge.com , subscriber-exclusive newsletters, and our ad-free podcast feed .We love hearing from you! Email your questions and thoughts to vergecast@theverge.com or call us at 866-VERGE11. Learn more about your ad choices. Visit podcastchoices.com/adchoices", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_c3b8ea20db52", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE The Vergecast: Online shopping is full of copycats -> Podcast: The Vergecast\nEpisode: Online shopping is full of copycats\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nThis week on The Vergecast, guest host Mia Sato talks to YouTube fitness pioneer Cassey Ho (better known as Blogilates) about the well-oiled machine that is the dup"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: The Vergecast Episode: Online shopping is full of copycats Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} This week on The Vergecast, guest host Mia Sato talks to YouTube fitness pioneer Cassey Ho (better known as Blogilates) about the well-oiled machine that is the dupe economy. Ho shares her experience creating her own line of athletic wear that sooner or later gets ripped off by countless copycats — and how she tries to fight back. Then, Mia brings an audio diary from a visit to Fabscrap, a textile recycling facility in Brooklyn, that is working to save fabric and other materials from the landfill. Fashion is a wasteful industry, not unlike tech — luckily, there are people like Fabscrap staff and volunteers who are working towards solutions. Finally, Victoria Song swings by to help answer a hotline question about how to make the high-tech Clueless closet a reality. If you have a question for us, call 866-VERGE11 or email vergecast@theverge.com . Further reading How dupes turned online shopping upside down Lululemon sues Costco over viral alleged “dupes” The US finally acknowledges textile waste in new report Your stuff is actually worse now Ghana becomes dumping ground for the world’s unwanted used clothes Learn more about your ad choices. Visit podcastchoices.com/adchoices", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_9e5b13afdf3d", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE The Vergecast: Switch 2, Steam Deck, and the next-gen console wars -> Podcast: The Vergecast\nEpisode: Switch 2, Steam Deck, and the next-gen console wars\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nHandheld gaming looks like the future — so why isn't it more popular? The Verge's Sean Hollister joins the show to talk about some new data about th"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: The Vergecast Episode: Switch 2, Steam Deck, and the next-gen console wars Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} Handheld gaming looks like the future — so why isn't it more popular? The Verge's Sean Hollister joins the show to talk about some new data about the handheld console market, what it says about the Steam Deck's dominance, whether the Switch 2 might change everything all over again, and why Sony and Microsoft don't appear to be in the game at all. After that, David reports on his trip to Florida to see TGL, the golf league aiming to bring the sport to new places and new fans, with the help of a truly enormous amount of technology. Finally, we answer a question on the Vergecast Hotline about iPads — and more specifically, one particularly good reason to upgrade to the Air or the Pro. Further reading: Three years later, the Steam Deck has dominated handheld PC gaming shipments Steam Deck OLED review: better, not faster Lenovo Legion Go S review: feels good, plays bad Asus ROG Ally X review: the best Windows gaming handheld by a mile MSI Claw review: an embarrassment A night at TGL, the would-be future of golf From ESPN : Inside the making of Tiger Woods and Rory McIlroy's TGL From Wired : Robotic Putting Greens. Mixed Reality. Loud Spectators. This Is Golf?! Apple iPad Air 2025 review: what the M3 upgrade really gets you Email us at vergecast@theverge.com or call us at 866-VERGE11, we love hearing from you. Learn more about your ad choices. Visit podcastchoices.com/adchoices", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_33d856fe3f2b", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE The Vergecast: How DeepSeek crashed the AI party -> Podcast: The Vergecast\nEpisode: How DeepSeek crashed the AI party\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nNilay and David dig into the week's biggest story: the new Intel-powered Surface Pro. Kidding! They talk about DeepSeek, the out-of-nowhere AI company that sent both "], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: The Vergecast Episode: How DeepSeek crashed the AI party Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} Nilay and David dig into the week's biggest story: the new Intel-powered Surface Pro. Kidding! They talk about DeepSeek, the out-of-nowhere AI company that sent both Silicon Valley and the stock market into uproar this week. Then, after the hosts debate what the real killer app for AI is — and whether we've even found one yet — we follow up on our question from last week about how people are actually using AI. We got so many good answers, and we talk through what to make of them all. Finally, in the lightning round, we talk about Brendan Carr being a dummy, the return of the Pebble, the continued rise of Bluesky and Threads, and Meta's $25 million check to Trump. Further reading: Why everyone is freaking out about DeepSeek DeepSeek says its newest AI model, Janus-Pro can outperform Stable Diffusion and DALL-E 3. Microsoft makes DeepSeek’s R1 model available on Azure AI and GitHub OpenAI has evidence that its models helped train China’s DeepSeek China’s DeepSeek AI is hitting Nvidia where it hurts DeepSeek’s AI app is restricting sign-ups due to ‘malicious attacks’ US Navy jumps the DeepSeek ship. DeepSeek wakes up Trump. OpenAI CEO Sam Altman on DeepSeek R1: “an impressive model.” Mark Zuckerberg tells Meta investors to not worry about DeepSeek The Pebble smartwatch is making a comeback, with some help from Google Oracle and Microsoft are reportedly in talks to take over TikTok FCC chair says landlords can force bulk internet service on residents From NYT: F.C.C. Chair Orders Investigation Into NPR and PBS Sponsorships Meta agrees to pay $25 million to settle Trump account suspension suit Zuckerberg wants to Make Facebook Great Again Zuck wants to bring the “OG Facebook” back. Email us at vergecast@theverge.com or call us at 866-VERGE11, we love hearing from you. Learn more about your ad choices. Visit podcastchoices.com/adchoices", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_49ebb6c59315", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE NPR: Do prediction market bettors make anything better? -> Podcast: NPR\nEpisode: Do prediction market bettors make anything better?\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: NPR Episode: Do prediction market bettors make anything better? Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}", "_quality": 0.6, "_expert": "expert_finance"}
{"id": "AEX_scraped_d6d7a07f3e45", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["HISTOIRE NPR: Venezuela’s recent economic history (Update) -> Podcast: NPR\nEpisode: Venezuela’s recent economic history (Update)\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: NPR Episode: Venezuela’s recent economic history (Update) Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}", "_quality": 0.6, "_expert": "expert_finance"}
{"id": "AEX_scraped_7cb6dee25bfb", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE BBC Global News: Jeffrey Epstein, Howard Lutnick, and the disgraced former prince -> Podcast: BBC Global News\nEpisode: Jeffrey Epstein, Howard Lutnick, and the disgraced former prince\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nIn an exclusive investigation, a whistleblower has told the BBC how he unearthed evidence suggesting that US Commerce Secretary Howar"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: BBC Global News Episode: Jeffrey Epstein, Howard Lutnick, and the disgraced former prince Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} In an exclusive investigation, a whistleblower has told the BBC how he unearthed evidence suggesting that US Commerce Secretary Howard Lutnick failed to disclose a business relationship with the paedophile financier, Jeffrey Epstein. Simon Andriesz was a trader at one of Lutnick’s Wall Street firms, but it wasn’t until the Epstein files were released earlier this year that he says he confirmed a longstanding suspicion that his boss was misrepresenting his relationship with Epstein. BBC financial investigations correspondent Andy Verity joins us to unpack the new investigation. For more episodes, just search 'The Global Story' wherever you get your BBC Podcasts.", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_286efa0aa0d0", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE BBC Global News: Trump scraps threat of 20% fee on Hormuz cargo -> Podcast: BBC Global News\nEpisode: Trump scraps threat of 20% fee on Hormuz cargo\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nDonald Trump says he's no longer planning to charge ships for using the Strait of Hormuz, as the US battles to break Iran's hold on the waterway. Trump"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: BBC Global News Episode: Trump scraps threat of 20% fee on Hormuz cargo Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} Donald Trump says he's no longer planning to charge ships for using the Strait of Hormuz, as the US battles to break Iran's hold on the waterway. Trump said the toll would be replaced by \"massive\" trade and investment deals with Gulf states. Also: US banks are making huge profits despite rising inflation. US Immigration and Customs Enforcement (ICE) agents have been told to halt most vehicle stops nationwide immediately, following a pair of fatal shootings in Texas and Maine involving ICE agents. Another record-breaking heatwave has hit parts of Canada and the US, bringing extreme temperatures and poor air quality. At the FIFA World Cup, Spain have beaten France to secure a place in the final. A 67-million-year-old Tyrannosaurus Rex has become the most expensive dinosaur fossil ever. The Global News Podcast brings you the breaking news you need to hear, as it happens. Listen for the latest headlines and current affairs from around the world. Politics, economics, climate, business, technology, health – we cover it all with expert analysis and insight. Get the news that matters, delivered twice a day on weekdays and daily at weekends, plus special bonus episodes reacting to urgent breaking stories. Follow or subscribe now and never miss a moment. Get in touch: globalpodcast@bbc.co.uk Photo: A ship using the Strait of Hormuz Credit: Reuters", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_51c139974646", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE BBC Global News: UK defence secretary resigns over military spending -> Podcast: BBC Global News\nEpisode: UK defence secretary resigns over military spending\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nBritain's defence secretary, John Healey, has resigned, accusing Prime Minister Keir Starmer of failing to commit the resources needed to protect "], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: BBC Global News Episode: UK defence secretary resigns over military spending Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} Britain's defence secretary, John Healey, has resigned, accusing Prime Minister Keir Starmer of failing to commit the resources needed to protect the nation from rising threats. Mr Healey said a long-delayed investment plan fell well short of what was required to bring defence spending to 3% of GDP by 2030 - a NATO target. He highlighted Mr Starmer's recent warning that Russia could attack the alliance as soon as that year. Also: Iran says its ceasefire with the US is now practically meaningless after a second night of airstrikes. Pope Leo is visiting the Canary Islands and meeting migrants who survived the Atlantic crossing to reach Spanish territory. With the World Cup about to kick off in Mexico City, we get a look behind the scenes at the Azteca Stadium. Australia begins a huge inquiry into unsolved murders and disappearances. New research reveals that people have a natural tendency to veer to the left when walking. And we hear about one woman’s mission to spot every butterfly species in Denmark - and what she discovered along the way. The Global News Podcast brings you the breaking news you need to hear, as it happens. Listen for the latest headlines and current affairs from around the world. Politics, economics, climate, business, technology, health – we cover it all with expert analysis and insight. Get the news that matters, delivered twice a day on weekdays and daily at weekends, plus special bonus episodes reacting to urgent breaking stories. Follow or subscribe now and never miss a moment. Get in touch: globalpodcast@bbc.co.uk Photo: John Healey, who has resigned as Britain's defence secretary, delivering a speech at an event in May Credit: PA", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_ba23d127e2f0", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE BBC Global News: Trump hails 'fantastic' new trade deals with China -> Podcast: BBC Global News\nEpisode: Trump hails 'fantastic' new trade deals with China\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nDonald Trump has left China after talks with Xi Jinping that the US president says have settled \"a lot of different problems\". President Xi called "], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: BBC Global News Episode: Trump hails 'fantastic' new trade deals with China Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} Donald Trump has left China after talks with Xi Jinping that the US president says have settled \"a lot of different problems\". President Xi called it a \"landmark\" visit, but gave no details of any new agreements. On Iran, President Trump said the Chinese president would pressure Tehran to re-open the Strait of Hormuz. Mr Trump also said he didn't think there was a risk of conflict between China and the US over Taiwan. Also: American media reports say the US is preparing to indict the former Cuban president, 94-year-old Raul Castro. There's been a large prisoner swap between Ukraine and Russia as the two sides ramp up their aerial attacks. The BBC has traced dozens of social media accounts - which post AI-generated anti-immigration content about the UK - to countries such as Sri Lanka and Vietnam. And the singer-songwriter with Parkinson's disease using AI to help him make music again. The Global News Podcast brings you the breaking news you need to hear, as it happens. Listen for the latest headlines and current affairs from around the world. Politics, economics, climate, business, technology, health – we cover it all with expert analysis and insight. Get the news that matters, delivered twice a day on weekdays and daily at weekends, plus special bonus episodes reacting to urgent breaking stories. Follow or subscribe now and never miss a moment. Get in touch: globalpodcast@bbc.co.uk", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_462625d07bd0", "k": "MODULE_FINANCE", "domain": "FINANCE", "authority": 0.7, "depends_on": ["MODULE_MATH", "MODULE_CS"], "instr": {"op": "RISK_MODELING", "args": [{"name": "portfolio_composition", "type": "string", "desc": "Composition du portefeuille"}, {"name": "market_conditions", "type": "string", "desc": "Conditions du marché"}], "out": ["var_95", "expected_shortfall"]}, "examples": ["FINANCE BBC Global News: Trump heads to China for crucial talks with Xi Jinping -> Podcast: BBC Global News\nEpisode: Trump heads to China for crucial talks with Xi Jinping\nDuration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'}\n\nPresident Trump is travelling to Beijing, on the first trip to China by a US leader since he last went there in 2017. The main focus of his hig"], "rag_signature": "RISK_MODELING portfolio_composition market_conditions var_95 expected_shortfall", "speech_prompt": "Instruction: Analyser et répondre sur le domaine FINANCE. Output: réponse structurée.", "knowledge": "Podcast: BBC Global News Episode: Trump heads to China for crucial talks with Xi Jinping Duration: {'itunes': 'http://www.itunes.com/dtds/podcast-1.0.dtd'} President Trump is travelling to Beijing, on the first trip to China by a US leader since he last went there in 2017. The main focus of his highly significant talks with Xi Jinping will be the tense trade relationship between the two superpowers. President Trump said he would also have a \"long chat\" with President Xi about Iran. Also: we have the latest from the court battle between two of the biggest names in tech - Elon Musk and Sam Altman. How just one infusion of immune cells could suppress HIV for years. The viral video of a mafia-linked illegal horse race in Sicily - escorted by armed scooter riders - that's led police to seize the animals and launch an investigation. And Israel makes it through to the final of the Eurovision Song Contest, as five countries boycott the event over the war in Gaza. The Global News Podcast brings you the breaking news you need to hear, as it happens. Listen for the latest headlines and current affairs from around the world. Politics, economics, climate, business, technology, health – we cover it all with expert analysis and insight. Get the news that matters, delivered twice a day on weekdays and daily at weekends, plus special bonus episodes reacting to urgent breaking stories. Follow or subscribe now and never miss a moment. Get in touch: globalpodcast@bbc.co.uk FILE PHOTO: U.S. President Donald Trump shakes hands with Chinese President Xi Jinping as they hold a bilateral meeting at Gimhae International Airport, on the sidelines of the Asia-Pacific Economic Cooperation (APEC) summit, in Busan, South Korea, October 30, 2025. REUTERS/Evelyn Hockstein/File Photo", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_510840b26dbf4285", "k": "FINANCE_QuantConnect/Lean_-_Lean_Algorithmic_Trading_Engine_by_QuantConnect_(Python,_C#)", "domain": "FINANCE", "authority": 0.7, "depends_on": [], "instr": {"op": "QA_FINANCE", "args": [{"name": "question", "type": "string"}], "out": ["response"]}, "examples": ["FINANCE QuantConnect/Lean - Lean Algorithmic Trading Engine by QuantConnect (Python, C#) -> Repository: QuantConnect/Lean\nLanguage: C#\nStars: 20839\nTopics: algorithm,algorithmic-trading-engine,c-sharp,finance,forex,lean-engine,options,python,quantconnect,stock-indicators,trading,trading-algorithms,trading-bot,trading-platform,trading-strategies\nDescription: Lean Algorithmic Trading Engine ..."], "rag_signature": "FINANCE response", "speech_prompt": "Instruction: Réponds sur FINANCE.", "knowledge": "Repository: QuantConnect/Lean Language: C# Stars: 20839 Topics: algorithm,algorithmic-trading-engine,c-sharp,finance,forex,lean-engine,options,python,quantconnect,stock-indicators,trading,trading-algorithms,trading-bot,trading-platform,trading-strategies Description: Lean Algorithmic Trading Engine by QuantConnect (Python, C#) README:", "prompt": "Expert FINANCE. Réponds:\n\nRepository: QuantConnect/Lean\nLanguage: C#\nStars: 20839\nTopics: algorithm,algorithmic-trading-engine,c-sharp,finance,forex,lean-engine,options,python,quantconnect,stock-indicators,trading,trading-algorithms,trading-bot,trading-platform,trading-strategies\nDescription: Lean Algorithmic Trading Engine by QuantConnect (Python, C#)\n\nREADME:\n", "_source": "github", "_lang": "en", "_quality": 0.6, "_expert": "expert_finance"}
{"id": "AEX_scraped_5f6932383b6ed998", "k": "FINANCE_StockSharp/StockSharp_-_Algorithmic_trading_and_quantitative_trading_open_source", "domain": "FINANCE", "authority": 0.7, "depends_on": [], "instr": {"op": "QA_FINANCE", "args": [{"name": "question", "type": "string"}], "out": ["response"]}, "examples": ["FINANCE StockSharp/StockSharp - Algorithmic trading and quantitative trading open source platform to develop trading robots (stock m -> Repository: StockSharp/StockSharp\nLanguage: C#\nStars: 10420\nTopics: algorithmic-trading,algorithmic-trading-engine,backtesting,bitcoins,broker,brokers,c-sharp,crypto,cryptocurrency,finance,fixprotocol,forex,hft-trading,interactive-brokers,quantitative-finance,stocks,trading,trading-platform,trading-..."], "rag_signature": "FINANCE response", "speech_prompt": "Instruction: Réponds sur FINANCE.", "knowledge": "Repository: StockSharp/StockSharp Language: C# Stars: 10420 Topics: algorithmic-trading,algorithmic-trading-engine,backtesting,bitcoins,broker,brokers,c-sharp,crypto,cryptocurrency,finance,fixprotocol,forex,hft-trading,interactive-brokers,quantitative-finance,stocks,trading,trading-platform,trading-robots,trading-strategies Description: Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options). README: # [StockSharp - trading platform][1] ## **English** | [ ](README.ru.md) | [ ](README.zh.md) ## Docs Download Chat YouTube ## Introduction ## **StockSharp** (**S#** for short) is a **free** platform for trading on any market in the world (crypto exchanges, American, European, Asian, Russian, stocks, futures, options, Bitcoins, forex, etc.). You will be able to trade manually or automatically (algorithmic trading robots, conventional or HFT). **Available connections**: Binance, MT4, MT5, FIX/FAST, PolygonIO, Trading Technologies, Alpaca Markets, BarChart, CQG, E*Trade, IQFeed, InteractiveBrokers, LMAX, MatLab, Oanda, FXCM, Rithmic, cTrader, DXtrade, BitStamp, Bitfinex, Coinbase, Kraken, Poloniex, GDAX, Bittrex, Bithumb, OKX, Coincheck, CEX.IO, BitMEX, YoBit, Livecoin, EXMO, Deribit, HTX, KuCoin, QuantFEED, Aster, edgeX, Ligther, Paradex, Hyperliquid and many others. Connector source code and the full connector list are available in the [StockSharp Connectors repository](https://github.com/StockSharp/Connectors). ## [Designer][8] **Designer** - a **free** universal algorithmic strategy application for easy strategy creation: - Visual designer to create strategies by mouse clicking - Embedded C# editor - Easy to create own indicators - Built-in debugger - Connections to the multiple electronic boards and brokers - All world platforms - Schema sharing with own team ## [Hydra][9] **Hydra** - **free** software to automatically load and store market data: - Supports many sources - High compression ratio - Any data type - Program access to stored data via API - Export to csv, excel, xml or database - Import from csv - Scheduled tasks - Auto-sync over the Internet between several Hydra instances ## [Terminal][10] **Terminal** - a **free** trading charting application (trading terminal): - Connections to the multiple electronic boards and brokers - Trading from charts by clicking - Arbitrary timeframes - Volume, Tick, Range, P&F, Renko candles - Cluster charts - Box charts - Volume Profile ## [Shell][11] **Shell** - the ready-made graphical framework with the ability to quickly adapt to your needs and with fully open source code in C#: - Complete source code - Support for all StockSharp platform connections - Support for Designer schemas - Flexible user interface - Strategy testing (statistics, equity, reports) - Save and load strategy settings - Launch strategies in parallel - Detailed information", "prompt": "Expert FINANCE. Réponds:\n\nRepository: StockSharp/StockSharp\nLanguage: C#\nStars: 10420\nTopics: algorithmic-trading,algorithmic-trading-engine,backtesting,bitcoins,broker,brokers,c-sharp,crypto,cryptocurrency,finance,fixprotocol,forex,hft-trading,interactive-brokers,quantitative-finance,stocks,trading,trading-platform,trading-robots,trading-strategies\nDescription: Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).\n\nREADME:\n![](\"./Media/SLogo.png\")
\n\n# [StockSharp - trading platform][1]\n\n## **English** | [ ](README.ru.md) | [ ](README.zh.md)\n\n## ![\"Docs\"](\"https://raw.githubusercontent.com/twitter/twemoji/master/assets/svg/1f4d6.svg\")
Docs ![\"Download\"](\"https://raw.githubusercontent.com/twitter/twemoji/master/assets/svg/1f4be.svg\")
Download ![\"Chat\"](\"https://raw.githubusercontent.com/twitter/twemoji/master/assets/svg/1f4ac.svg\")
Chat ![\"YouTube\"](\"https://raw.githubusercontent.com/edent/SuperTinyIcons/master/images/svg/youtube.svg\")
YouTube\n\n## Introduction ##\n\n**StockSharp** (**S#** for short) is a **free** platform for trading on any market in the world (crypto exchanges, American, European, Asian, Russian, stocks, futures, options, Bitcoins, forex, etc.). You will be able to trade manually or automatically (algorithmic trading robots, conventional or HFT).\n\n**Available connections**: Binance, MT4, MT5, FIX/FAST, PolygonIO, Trading Technologies, Alpaca Markets, BarChart, CQG, E*Trade, IQFeed, InteractiveBrokers, LMAX, MatLab, Oanda, FXCM, Rithmic, cTrader, DXtrade, BitStamp, Bitfinex, Coinbase, Kraken, Polonie", "_source": "github", "_lang": "en", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_807bb3c0024ff650", "k": "FINANCE_wangzhe3224/awesome-systematic-trading_-_A_curated_list_of_insanely_awesome_libr", "domain": "FINANCE", "authority": 0.7, "depends_on": [], "instr": {"op": "QA_FINANCE", "args": [{"name": "question", "type": "string"}], "out": ["response"]}, "examples": ["FINANCE wangzhe3224/awesome-systematic-trading - A curated list of insanely awesome libraries, packages and resources for systematic trading. Crypto, -> Repository: wangzhe3224/awesome-systematic-trading\nLanguage: HTML\nStars: 4529\nTopics: algorithmic-trading,alpha,awesome-list,backtesting,bitcoin,cryptocurrencies,cryptocurrency,finance,finances,golang,python,quant,quantitative-trading,rust,systematic-trading,systematic-trading-strategies,trading,tra..."], "rag_signature": "FINANCE response", "speech_prompt": "Instruction: Réponds sur FINANCE.", "knowledge": "Repository: wangzhe3224/awesome-systematic-trading Language: HTML Stars: 4529 Topics: algorithmic-trading,alpha,awesome-list,backtesting,bitcoin,cryptocurrencies,cryptocurrency,finance,finances,golang,python,quant,quantitative-trading,rust,systematic-trading,systematic-trading-strategies,trading,trading-algorithms,trading-bot,trading-strategies Description: A curated list of insanely awesome libraries, packages and resources for systematic trading. Crypto, Stock, Futures, Options, CFDs, FX, and more | 量化交易 | 量化投资 README:", "prompt": "Expert FINANCE. Réponds:\n\nRepository: wangzhe3224/awesome-systematic-trading\nLanguage: HTML\nStars: 4529\nTopics: algorithmic-trading,alpha,awesome-list,backtesting,bitcoin,cryptocurrencies,cryptocurrency,finance,finances,golang,python,quant,quantitative-trading,rust,systematic-trading,systematic-trading-strategies,trading,trading-algorithms,trading-bot,trading-strategies\nDescription: A curated list of insanely awesome libraries, packages and resources for systematic trading. Crypto, Stock, Futures, Options, CFDs, FX, and more | 量化交易 | 量化投资\n\nREADME:\n", "_source": "github", "_lang": "en", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_scraped_257c5d2f2c6c2069", "k": "FINANCE_The-Swarm-Corporation/AutoHedge_-_Build_your_autonomous_hedge_fund_in_minutes._A", "domain": "FINANCE", "authority": 0.7, "depends_on": [], "instr": {"op": "QA_FINANCE", "args": [{"name": "question", "type": "string"}], "out": ["response"]}, "examples": ["FINANCE The-Swarm-Corporation/AutoHedge - Build your autonomous hedge fund in minutes. AutoHedge harnesses the power of swarm intelligence and -> Repository: The-Swarm-Corporation/AutoHedge\nLanguage: Python\nStars: 4022\nTopics: ai,blackrock,finance,goldmansachs,hedgefund,jpmorgan,ml,quant,quantitative-finance,quantitative-trading,trading\nDescription: Build your autonomous hedge fund in minutes. AutoHedge harnesses the power of swarm intelligen..."], "rag_signature": "FINANCE response", "speech_prompt": "Instruction: Réponds sur FINANCE.", "knowledge": "Repository: The-Swarm-Corporation/AutoHedge Language: Python Stars: 4022 Topics: ai,blackrock,finance,goldmansachs,hedgefund,jpmorgan,ml,quant,quantitative-finance,quantitative-trading,trading Description: Build your autonomous hedge fund in minutes. AutoHedge harnesses the power of swarm intelligence and AI agents to automate market analysis, risk management, and trade execution. README: # AutoHedge [](https://discord.gg/VapjxpSyHC3) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/swarms_corp) AutoHedge is an enterprise-grade autonomous agent hedge fund that trades on your behalf. It combines swarm intelligence and specialized AI agents to perform end-to-end market analysis, risk management, and execution with minimal human intervention. **Current support:** Full autonomous trading on Solana. **Coming soon:** Coinbase and additional exchanges. --- ## Overview AutoHedge is built to be the world's most powerful autonomous agent hedge fund. It runs continuous analysis, generates and validates trading theses, sizes risk, and executes orders across supported venues. The system is designed for institutional reliability: structured outputs, comprehensive logging, and a risk-first architecture that scales from single strategies to multi-venue, multi-asset deployment. --- ## Features - **Multi-Agent Architecture**: Specialized agents for each stage of the trading pipeline - Director Agent: strategy and thesis generation - Quant Agent: technical and statistical analysis - Risk Management Agent: position sizing and risk assessment - Execution Agent: order generation and execution - **Real-Time Market Analysis**: Integration with live market data for analysis and execution - **Risk-First Design**: Built-in risk management and position sizing before any execution - **Structured Output**: JSON-formatted recommendations and analysis for downstream systems - **Enterprise Logging**: Detailed, configurable logging for audit and debugging - **Extensible Framework**: Modular design for custom strategies and new venues --- ## Supported Venues | Venue | Status | Notes | |-----------|----------------|--------------------------| | Solana | Supported | Full autonomous trading | | Coinbase | Coming soon | In development | | Other CEX | Roadmap | Planned expansion | --- ## Quick Start ### Installation ```bash pip install -U autohedge ``` ### Environment Variables ```bash # Jupiter API (token price & search tools) # Get a key at https://portal.jup.ag JUPITER_API_KEY= # OpenAI (experimental agents) OPENAI_API_KEY= ANTHROPIC_API_KEY= WORKSPACE_DIR=\"agent_workspace\" # Trading WALLET_PRIVATE_KEY=\"\" ``` See `.env.example` for a full reference. ### Basic Usage ```python autohedge ``` --- ## Architecture AutoHedge uses a multi-agent pipeline where each agent has a defined responsibility: ```mermaid graph TD A[Director Agent] --> B[Quant Agent] B --> C[Risk Manager] C --> D[Execution Agent] D --> E[Trade Output] ``` --- ## Contributing Contributions are welcome. See [Contributing Guidelines](CONTRIBUTING.md) for details. 1. Fork the repository 2. Create a feature branch (`git checkout -b feature/AmazingFeature`) 3. Commit changes (`git commit -m 'Add some AmazingFeature'`) 4. Push to the branch (`git push origin feature/AmazingFeature`) 5. Open a Pull Request --- ## License MIT License. See [", "prompt": "Expert FINANCE. Réponds:\n\nRepository: The-Swarm-Corporation/AutoHedge\nLanguage: Python\nStars: 4022\nTopics: ai,blackrock,finance,goldmansachs,hedgefund,jpmorgan,ml,quant,quantitative-finance,quantitative-trading,trading\nDescription: Build your autonomous hedge fund in minutes. AutoHedge harnesses the power of swarm intelligence and AI agents to automate market analysis, risk management, and trade execution.\n\nREADME:\n# AutoHedge\n\n[](https://discord.gg/VapjxpSyHC3) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/swarms_corp)\n\n\nAutoHedge is an enterprise-grade autonomous agent hedge fund that trades on your behalf. It combines swarm intelligence and specialized AI agents to perform end-to-end market analysis, risk management, and execution with minimal human intervention.\n\n**Current support:** Full autonomous trading on Solana. **Coming soon:** Coinbase and additional exchanges.\n\n---\n\n## Overview\n\nAutoHedge is built to be the world's most powerful autonomous agent hedge fund. It runs continuous analysis, generates and validates trading theses, sizes risk, and executes orders across supported venues. The system is designed for institutional reliability: structured outputs, comprehensive logging, and a risk-first architecture that scales from single strategies to multi-venue, multi-asset deployment.\n\n---\n\n## Features\n\n- **Multi-Agent Architecture**: Specialized agents for each stage of the trading pipeline\n - Director Agent: strategy and thesis generation", "_source": "github", "_lang": "en", "_quality": 0.7, "_expert": "expert_finance"}
{"id": "AEX_scraped_772e5b815155ded3", "k": "FINANCE_austin-starks/NextTrade_-_A_system_that_performs_algorithmic_trading", "domain": "FINANCE", "authority": 0.7, "depends_on": [], "instr": {"op": "QA_FINANCE", "args": [{"name": "question", "type": "string"}], "out": ["response"]}, "examples": ["FINANCE austin-starks/NextTrade - A system that performs algorithmic trading -> Repository: austin-starks/NextTrade\nLanguage: TypeScript\nStars: 1776\nTopics: automated-trading,finance,frontend,investing,software,stock-market,trading,trading-algorithms,trading-strategies,tradingbot,ui\nDescription: A system that performs algorithmic trading\n\nREADME:\n# NextTrade - Plan your next tr..."], "rag_signature": "FINANCE response", "speech_prompt": "Instruction: Réponds sur FINANCE.", "knowledge": "Repository: austin-starks/NextTrade Language: TypeScript Stars: 1776 Topics: automated-trading,finance,frontend,investing,software,stock-market,trading,trading-algorithms,trading-strategies,tradingbot,ui Description: A system that performs algorithmic trading README: # NextTrade - Plan your next trade A system to create, test, optimize, and deploy algorithmic trading strategies. For a better, faster, cloud-based platform, [check out NexusTrade](https://nexustrade.io/). If you want to learn about algorithmic trading and financial research from a community of data-driven investors, [join our free Discord channel!](https://nexustrade.io/discord) NexusTrade is a AI-Powered platform that streamlines the configuration of trading strategies. It's faster, more configurable, and more feature-rich than NextTrade. It features a powerful AI Chat Assistant tool as an entry point to many of its features. Some improvements include: - Enhanced configurability: Can essentially express any trading idea you can imagine - AI-Powered Research Tools: Perform in-depth company analysis and compare companies between each other - Powerful stock screener: Use natural language to find stocks in over 130 industries and 30+ indicators * [Read More about NextTrade](https://medium.com/@austin-starks/i-created-an-open-source-automated-trading-platform-f9d94575ceba) * [Comparing NextTrade to NexusTrade Part 1](https://medium.com/p/317b6e068254) * [Comparing NextTrade to NexusTrade Part 2](https://medium.com/p/cfa9bab0ee85) ## NextTrade Features 1. Combine conditions to form compound conditions. Combine compound conditions and create trading strategies. -  - For example: - Condition A: QQQ stock is 1 SD below its 5-day mean price - Condition B: Buying power is above $8,000. - Condition C: Condition A and condition B - Strategy: If condition C, buy $3000 of SPY 2. Create unlimited portfolios with different combinations of strategies -  4. Backtest those strategies using historical data. -  - Only stocks are currently supported, but cryptocurrency and options support is baked into the architecture 6. Optimize the strategy's parameters using a genetic algorithm -  - Choose hyperparameters like mutation rate, training period, validation period, and population size - Choose to optimize percent gain, sortino ratio, sharpe ratio, or max drawdown 8. Deploy the strategies live and see how it performs in real-time -  ## System Architecture and Design Some architectural artifacts can be found at this link: https://drive.google.com/drive/folders/1TgZNGPd7TBWi47dWh0TI2nZ_9WUhv_P_?usp=sharing ## Local Installation ### Prerequisites 1. Mac, Linux or WSL2 preferred - [How to set up Linux on Windows with WSL 2](https://s1gr1d.medium.com/how-to-set-up-linux-on-windows-with-wsl-2-debe2a64d20d) 2. Tradier Account (with at least a free sandbox token; additional brokers TBD) - [Production vs Sandbox data details](https://documentation.tradier.com/brokerage-api/overview/market-data) - [Sandbox Account and Signup info](https://documentation.tradier.com/brokerage-api/overview/endpoints) ### Mac Setup 1. Clone the repository. 2. Install [MongoDB community edition](https://docs.mongodb.com/manual/administration/install-community/) and enable it. 3. Install the dependencies: `", "prompt": "Expert FINANCE. Réponds:\n\nRepository: austin-starks/NextTrade\nLanguage: TypeScript\nStars: 1776\nTopics: automated-trading,finance,frontend,investing,software,stock-market,trading,trading-algorithms,trading-strategies,tradingbot,ui\nDescription: A system that performs algorithmic trading\n\nREADME:\n# NextTrade - Plan your next trade\n\nA system to create, test, optimize, and deploy algorithmic trading strategies.\n\nFor a better, faster, cloud-based platform, [check out NexusTrade](https://nexustrade.io/). If you want to learn about algorithmic trading and financial research from a community of data-driven investors, [join our free Discord channel!](https://nexustrade.io/discord)\n\nNexusTrade is a AI-Powered platform that streamlines the configuration of trading strategies. It's faster, more configurable, and more feature-rich than NextTrade. It features a powerful AI Chat Assistant tool as an entry point to many of its features.\n\nSome improvements include:\n - Enhanced configurability: Can essentially express any trading idea you can imagine\n - AI-Powered Research Tools: Perform in-depth company analysis and compare companies between each other\n - Powerful stock screener: Use natural language to find stocks in over 130 industries and 30+ indicators\n\n* [Read More about NextTrade](https://medium.com/@austin-starks/i-created-an-open-source-automated-trading-platform-f9d94575ceba)\n* [Comparing NextTrade to NexusTrade Part 1](https://medium.com/p/317b6e068254)\n* [Comparing NextTrade to NexusTrade Part 2](https://medium.com/p/cfa9bab0ee85)\n\n## NextTrade Features\n\n1. Combine conditions to form compound conditions. Combine compound conditions and create trading strategies.\n - \n - For example:\n - Condition A: QQQ stock is 1 SD below its 5-day mean price\n - Condition B: Buying power is above $8,000.\n - Condition C: Condition A and condition B\n -", "_source": "github", "_lang": "en", "_quality": 0.7, "_expert": "expert_finance"}
{"id": "AEX_scraped_2087b0ed88c90551", "k": "FINANCE_longbridge/longbridge-terminal_-_AI-native_CLI_for_the_Longbridge_trading_platfo", "domain": "FINANCE", "authority": 0.6, "depends_on": [], "instr": {"op": "QA_FINANCE", "args": [{"name": "question", "type": "string"}], "out": ["response"]}, "examples": ["FINANCE longbridge/longbridge-terminal - AI-native CLI for the Longbridge trading platform with real-time market data, portfolio, and trading -> Repository: longbridge/longbridge-terminal\nLanguage: Rust\nStars: 951\nTopics: ai-native,cli,finance,financial-analysis,ratatui,tui\nDescription: AI-native CLI for the Longbridge trading platform with real-time market data, portfolio, and trading...\n\nREADME:\n# Longbridge Terminal\n\n\n ..."], "rag_signature": "FINANCE response", "speech_prompt": "Instruction: Réponds sur FINANCE.", "knowledge": "Repository: longbridge/longbridge-terminal Language: Rust Stars: 951 Topics: ai-native,cli,finance,financial-analysis,ratatui,tui Description: AI-native CLI for the Longbridge trading platform with real-time market data, portfolio, and trading... README: # Longbridge Terminal English | AI-native CLI for the [Longbridge](https://longbridge.com) trading platform real-time market data, portfolio, and trading. Also ships a full-screen TUI for interactive monitoring. Covers every Longbridge OpenAPI endpoint: real-time quotes, depth, K-lines, options, and warrants for market data; account balances, stock and fund positions for portfolio management; and order submission, modification, cancellation, and execution history for trading. Designed for scripting, AI-agent tool-calling, and daily trading workflows from the terminal. ```bash $ longbridge static TSLA.US NVDA.US | Symbol | Last | Prev Close | Open | High | Low | Volume | Turnover | Status | |---------|---------|------------|---------|---------|---------|-----------|-----------------|--------| | TSLA.US | 395.560 | 391.200 | 396.220 | 403.730 | 394.420 | 58068343 | 23138752546.000 | Normal | | NVDA.US | 183.220 | 180.250 | 182.970 | 188.880 | 181.410 | 217307380 | 40023702698.000 | Normal | $ longbridge quote TSLA.US NVDA.US --format json [ { \"high\": \"403.730\", \"last\": \"395.560\", \"low\": \"394.420\", \"open\": \"396.220\", \"prev_close\": \"391.200\", \"status\": \"Normal\", \"symbol\": \"TSLA.US\", \"turnover\": \"23138752546.000\", \"volume\": \"58068343\" }, { \"high\": \"188.880\", \"last\": \"183.220\", \"low\": \"181.410\", \"open\": \"182.970\", \"prev_close\": \"180.250\", \"status\": \"Normal\", \"symbol\": \"NVDA.US\", \"turnover\": \"40023702698.000\", \"volume\": \"217307380\" } ] ``` [](https://asciinema.org/a/785102) ## Installation **Homebrew (macOS / Linux)** ```bash brew install --cask longbridge/tap/longbridge-terminal ``` **Windows** ([Scoop](https://scoop.sh)) ```powershell scoop install https://github.com/longbridge/longbridge-terminal/raw/refs/heads/main/.scoop/longbridge.json ``` **Windows** (PowerShell) ```powershell iwr https://github.com/longbridge/longbridge-terminal/raw/main/install.ps1 | iex ``` **Install script (macOS / Linux)** ```bash curl -sSL https://github.com/longbridge/longbridge-terminal/raw/main/install | sh ``` Installs the `longbridge` binary to `/usr/local/bin` (macOS/Linux) or `%LOCALAPPDATA%\\Programs\\longbridge` (Windows). ## Authentication Uses **OAuth 2.0** via the Longbridge SDK no manual token management required. ```bash longbridge auth login # Opens browser for OAuth and saves token (managed by SDK) longbridge auth logout # Clear saved token longbridge check # Verify token, region, and API endpoint connectivity ``` Token is shared between CLI and TUI. After `login`, all commands work without re-authenticating. The CLI auto-detects China Mainland on each startup by probing `geotest.lbkrs.com` in the background and caches the result. If detected, CN API endpoints are used automatically on the next run. ## Shell Completion Enable tab-completion for `longbridge` commands and flags in your shell: **Bash** add to `~/.bashrc` or `~/.bash_profile`: ```bash source ` will suggest subcommands, flags, and values. ## CLI Usage ``` longbridge [options] ``` All commands support `--format json` for machine-readable output. Commands that accept `--count` also accept `--limit` as an alias (for AI agent compatibility): ```bash longbridge quote TSLA.US --format json longbridg", "prompt": "Expert FINANCE. Réponds:\n\nRepository: longbridge/longbridge-terminal\nLanguage: Rust\nStars: 951\nTopics: ai-native,cli,finance,financial-analysis,ratatui,tui\nDescription: AI-native CLI for the Longbridge trading platform with real-time market data, portfolio, and trading...\n\nREADME:\n# Longbridge Terminal\n\n
\n English | \n
\n\nAI-native CLI for the [Longbridge](https://longbridge.com) trading platform real-time market data, portfolio, and trading. Also ships a full-screen TUI for interactive monitoring.\n\nCovers every Longbridge OpenAPI endpoint: real-time quotes, depth, K-lines, options, and warrants for market data; account balances, stock and fund positions for portfolio management; and order submission, modification, cancellation, and execution history for trading. Designed for scripting, AI-agent tool-calling, and daily trading workflows from the terminal.\n\n```bash\n$ longbridge static TSLA.US NVDA.US\n| Symbol | Last | Prev Close | Open | High | Low | Volume | Turnover | Status |\n|---------|---------|------------|---------|---------|---------|-----------|-----------------|--------|\n| TSLA.US | 395.560 | 391.200 | 396.220 | 403.730 | 394.420 | 58068343 | 23138752546.000 | Normal |\n| NVDA.US | 183.220 | 180.250 | 182.970 | 188.880 | 181.410 | 217307380 | 40023702698.000 | Normal |\n\n$ longbridge quote TSLA.US NVDA.US --format json\n[\n {\n \"high\": \"403.730\",\n \"last\": \"395.560\",\n \"low\": \"394.420\",\n \"open\": \"396.220\",\n \"prev_close\": \"391.200\",\n \"status\": \"Normal\",\n \"symbol\": \"TSLA.US\",\n \"turnover\": \"23138752546.000\",\n \"volume\": \"58068343\"\n },\n {\n \"high\": \"188.880\",\n \"last\": \"183.220\",\n \"low\": \"181.410\",\n \"open\": \"182.970\",\n \"prev_close\": \"180.250\",\n \"status\": \"Normal\",\n \"symbol\": \"NVDA.US\",\n \"turnover\": \"40023702698.000\",\n \"volume\": \"217307380\"\n }\n]\n```\n\n[](https://asciinema.org/a/785102)\n\n## Inst", "_source": "github", "_lang": "en", "_quality": 0.7, "_expert": "expert_finance"}
{"id": "AEX_scraped_bd31280a32e3cbd2", "k": "FINANCE_constverum/Quantdom_-_Python-based_framework_for_backtesting_trading_strategies_", "domain": "FINANCE", "authority": 0.6, "depends_on": [], "instr": {"op": "QA_FINANCE", "args": [{"name": "question", "type": "string"}], "out": ["response"]}, "examples": ["FINANCE constverum/Quantdom - Python-based framework for backtesting trading strategies & analyzing financial markets [GUI :neckbe -> Repository: constverum/Quantdom\nLanguage: Python\nStars: 772\nTopics: algorithmic-trading,algotrading,backtesting,finance,financial-analysis,fintech,investment,quant,quantitative-finance,stock-market,trading,trading-platform,trading-strategies\nDescription: Python-based framework for backtesting tradin..."], "rag_signature": "FINANCE response", "speech_prompt": "Instruction: Réponds sur FINANCE.", "knowledge": "Repository: constverum/Quantdom Language: Python Stars: 772 Topics: algorithmic-trading,algotrading,backtesting,finance,financial-analysis,fintech,investment,quant,quantitative-finance,stock-market,trading,trading-platform,trading-strategies Description: Python-based framework for backtesting trading strategies & analyzing financial markets [GUI :neckbeard:] README:", "prompt": "Expert FINANCE. Réponds:\n\nRepository: constverum/Quantdom\nLanguage: Python\nStars: 772\nTopics: algorithmic-trading,algotrading,backtesting,finance,financial-analysis,fintech,investment,quant,quantitative-finance,stock-market,trading,trading-platform,trading-strategies\nDescription: Python-based framework for backtesting trading strategies & analyzing financial markets [GUI :neckbeard:]\n\nREADME:\n", "_source": "github", "_lang": "en", "_quality": 0.7, "_expert": "expert_finance"}
{"id": "AEX_github_c4b9465dca65", "k": "FINANCE_Code:_astral-sh/ruff/.config/nextest.toml", "domain": "FINANCE", "subdomain": "", "authority": 0.84, "depends_on": [], "instr": {"op": "QA_FINANCE", "args": [{"name": "question", "type": "string", "desc": "Question sur finance"}], "out": ["response"]}, "examples": ["FINANCE Code: astral-sh/ruff/.config/nextest.toml -> File: astral-sh/ruff/.config/nextest.toml\n\n# Define serial test group for running tests sequentially.\n[test-groups]\nserial = { max-threads = 1 }\n\n# Run ty file watching tests sequentially to avoid race conditions.\n[[profile.default.overrides]]\nfilter = 'binary(file_watching)'\ntest-group = 'serial'\n\n"], "rag_signature": "FINANCE response", "speech_prompt": "Instruction: Réponds à la question suivante sur FINANCE. Output: response.", "knowledge": "File: astral-sh/ruff/.config/nextest.toml # Define serial test group for running tests sequentially. [test-groups] serial = { max-threads = 1 } # Run ty file watching tests sequentially to avoid race conditions. [[profile.default.overrides]] filter = 'binary(file_watching)' test-group = 'serial' [profile.ci] # Print out output for failing tests as soon as they fail, and also at the end # of the run (for easy scrollability). failure-output = \"immediate-final\" # Do not cancel the test run on the first failure. fail-fast = false status-level = \"skip\" # Mark tests that take longer than 1s as slow. # Terminate after 60s as a stop-gap measure to terminate on deadlock. slow-timeout = { period = \"1s\", terminate-after = 60 } # Show slow jobs in the final summary final-status-level = \"slow\"", "prompt": "File: astral-sh/ruff/.config/nextest.toml\n\n# Define serial test group for running tests sequentially.\n[test-groups]\nserial = { max-threads = 1 }\n\n# Run ty file watching tests sequentially to avoid race conditions.\n[[profile.default.overrides]]\nfilter = 'binary(file_watching)'\ntest-group = 'serial'\n\n[profile.ci]\n# Print out output for failing tests as soon as they fail, and also at the end\n# of the run (for easy scrollability).\nfailure-output = \"immediate-final\"\n# Do not cancel the test run on the first failure.\nfail-fast = false\n\nstatus-level = \"skip\"\n\n# Mark tests that take longer than 1s as slow.\n# Terminate after 60s as a stop-gap measure to terminate on deadlock.\nslow-timeout = { period = \"1s\", terminate-after = 60 }\n\n# Show slow jobs in the final summary\nfinal-status-level = \"slow\"", "_source": "gh_code", "_lang": "toml", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_github_dd8de0bb7244", "k": "FINANCE_Code:_getsentry/sentry-python/.agents/skills/security-review/SKILL.md", "domain": "FINANCE", "subdomain": "", "authority": 0.84, "depends_on": [], "instr": {"op": "QA_FINANCE", "args": [{"name": "question", "type": "string", "desc": "Question sur finance"}], "out": ["response"]}, "examples": ["FINANCE Code: getsentry/sentry-python/.agents/skills/security-review/SKILL.md -> ONLY if URL is from user input redirect(request.GET['next']) # FLAG: User-controlled redirect redirect(settings.LOGIN_URL) # SAFE: Server-controlled config # Weak crypto - ONLY if used for security purposes hashlib.md5(file_content) # SAFE: File checksums, caching hashlib.md5(password) # FLAG: Passw"], "rag_signature": "FINANCE response", "speech_prompt": "Instruction: Réponds à la question suivante sur FINANCE. Output: response.", "knowledge": "ONLY if URL is from user input redirect(request.GET['next']) # FLAG: User-controlled redirect redirect(settings.LOGIN_URL) # SAFE: Server-controlled config # Weak crypto - ONLY if used for security purposes hashlib.md5(file_content) # SAFE: File checksums, caching hashlib.md5(password) # FLAG: Password hashing random.random() # SAFE: Non-security uses (UI, sampling) random.random() for token # FLAG: Security tokens need secrets module ``` --- ## Output Format ```markdown ## Security Review: [File/Component Name] ### Summary - **Findings**: X (Y Critical, Z High, ...) - **Risk Level**: Critical/High/Medium/Low - **Confidence**: High/Mixed ### Findings #### [VULN-001] [Vulnerability Type] (Severity) - **Location**: `file.py:123` - **Confidence**: High - **Issue**: [What the vulnerability is] - **Impact**: [What an attacker could do] - **Evidence**: ```python [Vulnerable code snippet] ``` - **Fix**: [How to remediate] ### Needs Verification #### [VERIFY-001] [Potential Issue] - **Location**: `file.py:456` - **Question**: [What needs to be verified] ``` If no vulnerabilities found, state: \"No high-confidence vulnerabilities identified.\" --- ## Reference Files ### Core Vulnerabilities (`references/`) | File | Covers | |------|--------| | `injection.md` | SQL, NoSQL, OS command, LDAP, template injection | | `xss.md` | Reflected, stored, DOM-based XSS | | `authorization.md` | Authorization, IDOR, privilege escalation | | `authentication.md` | Sessions, credentials, password storage | | `cryptography.md` | Algorithms, key management, randomness | | `deserialization.md` | Pickle, YAML, Java, PHP deserialization | | `file-security.md` | Path traversal, uploads, XXE | | `ssrf.md` | Server-side request forgery | | `csrf.md` | Cross-site request forgery | | `data-protection.md` | Secrets exposure, PII, logging | | `api-security.md` | REST, GraphQL, mass assignment | | `business-logic.md` | Race conditions, workflow bypass | | `modern-threats.md` | Prototype pollution, LLM injection, WebSocket | | `misconfiguration.md` | Headers, CORS, debug mode, defaults | | `error-handling.md` | Fail-open, information disclosure | | `supply-chain.md` | Dependencies, build security | | `logging.md` | Audit failures, log injection | ### Language Guides (`languages/`) - `python.md` - Django, Flask, FastAPI patterns - `javascript.md` - Node, Express, React, Vue, Next.js - `go.md` - Go-specific security patterns - `rust.md` - Rust unsafe blocks, FFI security - `java.md` - Spring, Java EE patterns ### Infrastructure (`infrastructure/`) - `docker.md` - Container security - `kubernetes.md` - K8s RBAC, secrets, policies - `terraform.md` - IaC security - `ci-cd.md` - Pipeline security - `cloud.md` - AWS/GCP/Azure security", "prompt": "ONLY if URL is from user input redirect(request.GET['next']) # FLAG: User-controlled redirect redirect(settings.LOGIN_URL) # SAFE: Server-controlled config # Weak crypto - ONLY if used for security purposes hashlib.md5(file_content) # SAFE: File checksums, caching hashlib.md5(password) # FLAG: Password hashing random.random() # SAFE: Non-security uses (UI, sampling) random.random() for token # FLAG: Security tokens need secrets module ``` --- ## Output Format ```markdown ## Security Review: [File/Component Name] ### Summary - **Findings**: X (Y Critical, Z High, ...) - **Risk Level**: Critical/High/Medium/Low - **Confidence**: High/Mixed ### Findings #### [VULN-001] [Vulnerability Type] (Severity) - **Location**: `file.py:123` - **Confidence**: High - **Issue**: [What the vulnerability is] - **Impact**: [What an attacker could do] - **Evidence**: ```python [Vulnerable code snippet] ``` - **Fix**: [How to remediate] ### Needs Verification #### [VERIFY-001] [Potential Issue] - **Location**: `file.py:456` - **Question**: [What needs to be verified] ``` If no vulnerabilities found, state: \"No high-confidence vulnerabilities identified.\" --- ## Reference Files ### Core Vulnerabilities (`references/`) | File | Covers | |------|--------| | `injection.md` | SQL, NoSQL, OS command, LDAP, template injection | | `xss.md` | Reflected, stored, DOM-based XSS | | `authorization.md` | Authorization, IDOR, privilege escalation | | `authentication.md` | Sessions, credentials, password storage | | `cryptography.md` | Algorithms, key management, randomness | | `deserialization.md` | Pickle, YAML, Java, PHP deserialization | | `file-security.md` | Path traversal, uploads, XXE | | `ssrf.md` | Server-side request forgery | | `csrf.md` | Cross-site request forgery | | `data-protection.md` | Secrets exposure, PII, logging | | `api-security.md` | REST, GraphQL, mass assignment | | `business-logic.md` | Race conditions, workflow bypass | | `modern-threats.md` | Prototype pollution, LLM inject", "_source": "gh_code", "_lang": "markdown", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_github_2579d23c5087", "k": "FINANCE_Code:_getsentry/sentry-python/.agents/skills/security-review/references/cryptogr", "domain": "FINANCE", "subdomain": "", "authority": 0.84, "depends_on": [], "instr": {"op": "QA_FINANCE", "args": [{"name": "question", "type": "string", "desc": "Question sur finance"}], "out": ["response"]}, "examples": ["FINANCE Code: getsentry/sentry-python/.agents/skills/security-review/references/cryptography.md -> checksum = hashlib.sha256(data).hexdigest() ``` ### Insufficient Key Size ```python # VULNERABLE: Short key key = b'short_key' # 9 bytes # SAFE: Adequate key length key = secrets.token_bytes(32) # 256 bits ``` ### Predictable IV/Nonce ```python # VULNERABLE: Reused or predictable nonce nonce = b'\\x0"], "rag_signature": "FINANCE response", "speech_prompt": "Instruction: Réponds à la question suivante sur FINANCE. Output: response.", "knowledge": "checksum = hashlib.sha256(data).hexdigest() ``` ### Insufficient Key Size ```python # VULNERABLE: Short key key = b'short_key' # 9 bytes # SAFE: Adequate key length key = secrets.token_bytes(32) # 256 bits ``` ### Predictable IV/Nonce ```python # VULNERABLE: Reused or predictable nonce nonce = b'\\x00' * 12 # Static nonce # VULNERABLE: Counter-based without persistence nonce = counter.to_bytes(12, 'big') # SAFE: Random nonce nonce = secrets.token_bytes(12) ``` ### ECB Mode Patterns ```python # VULNERABLE: ECB reveals patterns cipher = AES.new(key, AES.MODE_ECB) # SAFE: GCM hides patterns cipher = AES.new(key, AES.MODE_GCM, nonce=nonce) ``` ### Missing Authentication ```python # VULNERABLE: Encryption without authentication cipher = AES.new(key, AES.MODE_CBC, iv=iv) ciphertext = cipher.encrypt(pad(plaintext, 16)) # Vulnerable to bit-flipping, padding oracle # SAFE: Authenticated encryption cipher = AES.new(key, AES.MODE_GCM, nonce=nonce) ciphertext, tag = cipher.encrypt_and_digest(plaintext) ``` --- ## Grep Patterns for Detection ```bash # Weak algorithms grep -rn \"MD5\\|md5\\|SHA1\\|sha1\\|DES\\|des\\|RC4\\|rc4\" --include=\"*.py\" --include=\"*.js\" grep -rn \"MODE_ECB\\|ecb\" --include=\"*.py\" --include=\"*.js\" # Insecure random grep -rn \"Math\\.random\\|random\\.random\\|random\\.randint\" --include=\"*.py\" --include=\"*.js\" grep -rn \"mt_rand\\|rand()\" --include=\"*.php\" # Hardcoded keys grep -rn \"key\\s*=\\s*['\\\"]\" --include=\"*.py\" --include=\"*.js\" grep -rn \"secret\\s*=\\s*['\\\"]\" --include=\"*.py\" --include=\"*.js\" grep -rn \"AES\\.new.*b'\" --include=\"*.py\" # Static IVs/nonces grep -rn \"iv\\s*=\\s*b'\\|nonce\\s*=\\s*b'\" --include=\"*.py\" grep -rn \"\\\\x00.*\\\\x00.*\\\\x00\" --include=\"*.py\" # CBC without HMAC grep -rn \"MODE_CBC\" --include=\"*.py\" | grep -v \"hmac\\|mac\\|tag\" ``` --- ## Testing Checklist - [ ] No hardcoded keys/secrets in source code - [ ] Keys not committed to version control - [ ] Using modern algorithms (AES-GCM, RSA-2048+, SHA-256+) - [ ] CSPRNG used for all security-sensitive randomness - [ ] Keys stored securely (HSM, KMS, secrets manager) - [ ] Key rotation mechanism exists - [ ] No ECB mode usage - [ ] Authenticated encryption used (GCM, or encrypt-then-MAC) - [ ] Adequate key lengths (256-bit symmetric, 2048+ RSA) - [ ] IVs/nonces are random and never reused with same key --- ## References - [OWASP Cryptographic Storage Cheat Sheet](https://cheatsheetseries.owasp.org/cheatsheets/Cryptographic_Storage_Cheat_Sheet.html) - [OWASP Key Management Cheat Sheet](https://cheatsheetseries.owasp.org/cheatsheets/Key_Management_Cheat_Sheet.html) - [CWE-327: Use of Broken Crypto Algorithm](https://cwe.mitre.org/data/definitions/327.html) - [CWE-330: Insufficient Randomness](https://cwe.mitre.org/data/definitions/330.html) - [CWE-321: Hard-coded Cryptographic Key](https://cwe.mitre.org/data/definitions/321.html)", "prompt": "checksum = hashlib.sha256(data).hexdigest() ``` ### Insufficient Key Size ```python # VULNERABLE: Short key key = b'short_key' # 9 bytes # SAFE: Adequate key length key = secrets.token_bytes(32) # 256 bits ``` ### Predictable IV/Nonce ```python # VULNERABLE: Reused or predictable nonce nonce = b'\\x00' * 12 # Static nonce # VULNERABLE: Counter-based without persistence nonce = counter.to_bytes(12, 'big') # SAFE: Random nonce nonce = secrets.token_bytes(12) ``` ### ECB Mode Patterns ```python # VULNERABLE: ECB reveals patterns cipher = AES.new(key, AES.MODE_ECB) # SAFE: GCM hides patterns cipher = AES.new(key, AES.MODE_GCM, nonce=nonce) ``` ### Missing Authentication ```python # VULNERABLE: Encryption without authentication cipher = AES.new(key, AES.MODE_CBC, iv=iv) ciphertext = cipher.encrypt(pad(plaintext, 16)) # Vulnerable to bit-flipping, padding oracle # SAFE: Authenticated encryption cipher = AES.new(key, AES.MODE_GCM, nonce=nonce) ciphertext, tag = cipher.encrypt_and_digest(plaintext) ``` --- ## Grep Patterns for Detection ```bash # Weak algorithms grep -rn \"MD5\\|md5\\|SHA1\\|sha1\\|DES\\|des\\|RC4\\|rc4\" --include=\"*.py\" --include=\"*.js\" grep -rn \"MODE_ECB\\|ecb\" --include=\"*.py\" --include=\"*.js\" # Insecure random grep -rn \"Math\\.random\\|random\\.random\\|random\\.randint\" --include=\"*.py\" --include=\"*.js\" grep -rn \"mt_rand\\|rand()\" --include=\"*.php\" # Hardcoded keys grep -rn \"key\\s*=\\s*['\\\"]\" --include=\"*.py\" --include=\"*.js\" grep -rn \"secret\\s*=\\s*['\\\"]\" --include=\"*.py\" --include=\"*.js\" grep -rn \"AES\\.new.*b'\" --include=\"*.py\" # Static IVs/nonces grep -rn \"iv\\s*=\\s*b'\\|nonce\\s*=\\s*b'\" --include=\"*.py\" grep -rn \"\\\\x00.*\\\\x00.*\\\\x00\" --include=\"*.py\" # CBC without HMAC grep -rn \"MODE_CBC\" --include=\"*.py\" | grep -v \"hmac\\|mac\\|tag\" ``` --- ## Testing Checklist - [ ] No hardcoded keys/secrets in source code - [ ] Keys not committed to version control - [ ] Using modern algorithms (AES-GCM, RSA-2048+, SHA-256+) - [ ] CSPRNG used for all security-sensitive randomness - [ ", "_source": "gh_code", "_lang": "markdown", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "AEX_github_ac598eee8f56", "k": "FINANCE_Code:_vercel/next.js/.agents/skills/flags/SKILL.md", "domain": "FINANCE", "subdomain": "", "authority": 0.84, "depends_on": [], "instr": {"op": "QA_FINANCE", "args": [{"name": "question", "type": "string", "desc": "Question sur finance"}], "out": ["response"]}, "examples": ["FINANCE Code: vercel/next.js/.agents/skills/flags/SKILL.md -> Feature Flags Use this skill when adding or changing framework feature flags in Next.js internals. ## Required Wiring All flags need: `config-shared.ts` (type) → `config-schema.ts` (zod). If the flag is consumed in user-bundled code (client components, edge routes, `app-page.ts` template), also add "], "rag_signature": "FINANCE response", "speech_prompt": "Instruction: Réponds à la question suivante sur FINANCE. Output: response.", "knowledge": "Feature Flags Use this skill when adding or changing framework feature flags in Next.js internals. ## Required Wiring All flags need: `config-shared.ts` (type) → `config-schema.ts` (zod). If the flag is consumed in user-bundled code (client components, edge routes, `app-page.ts` template), also add it to `define-env.ts` for build-time injection. Runtime-only flags consumed exclusively in pre-compiled bundles can skip `define-env.ts`. ## Where the Flag Is Consumed **Client/bundled code only** (e.g. `__NEXT_PPR` in client components): `define-env.ts` is sufficient. Webpack/Turbopack replaces `process.env.X` at the user's build time. **Pre-compiled runtime bundles** (e.g. code in `app-render.tsx`): The flag must also be set as a real `process.env` var at runtime, because `app-render.tsx` runs from pre-compiled bundles where `define-env.ts` doesn't reach. Two approaches: - **Runtime env var**: Set in `next-server.ts` + `export/worker.ts`. Both code paths stay in one bundle. Simple but increases bundle size. - **Separate bundle variant**: Add DefinePlugin entry in `next-runtime.webpack-config.js` (scoped to `bundleType === 'app'`), new taskfile tasks, update `module.compiled.js` selector, and still set env var in `next-server.ts` + `export/worker.ts` for bundle selection. Eliminates dead code but adds build complexity. For runtime flags, also add the field to the `NextConfigRuntime` Pick type in `config-shared.ts`. ## Runtime-Bundle Model - Runtime bundles are built by `next-runtime.webpack-config.js` (rspack) via `taskfile.js` bundle tasks. - Bundle selection occurs at runtime in `src/server/route-modules/app-page/module.compiled.js` based on `process.env` vars. - Variants: `{turbo/webpack} × {experimental/stable/nodestreams/experimental-nodestreams} × {dev/prod}` = up to 16 bundles per route type. - `define-env.ts` affects user bundling, not pre-compiled runtime internals. - `process.env.X` checks in `app-render.tsx` are either replaced by DefinePlugin at runtime-bundle-build time, or read as actual env vars at server startup. They are NOT affected by the user's defines from `define-env.ts`. - **Gotcha**: DefinePlugin entries in `next-runtime.webpack-config.js` must be scoped to the correct `bundleType` (e.g. `app` only, not `server`) to avoid replacing assignment targets in `next-server.ts`. ## Related Skills - `$dce-edge` - DCE-safe require patterns and edge constraints - `$react-vendoring` - entry-base boundaries and vendored React - `$runtime-debug` - reproduction and verification workflow", "prompt": "Feature Flags Use this skill when adding or changing framework feature flags in Next.js internals. ## Required Wiring All flags need: `config-shared.ts` (type) → `config-schema.ts` (zod). If the flag is consumed in user-bundled code (client components, edge routes, `app-page.ts` template), also add it to `define-env.ts` for build-time injection. Runtime-only flags consumed exclusively in pre-compiled bundles can skip `define-env.ts`. ## Where the Flag Is Consumed **Client/bundled code only** (e.g. `__NEXT_PPR` in client components): `define-env.ts` is sufficient. Webpack/Turbopack replaces `process.env.X` at the user's build time. **Pre-compiled runtime bundles** (e.g. code in `app-render.tsx`): The flag must also be set as a real `process.env` var at runtime, because `app-render.tsx` runs from pre-compiled bundles where `define-env.ts` doesn't reach. Two approaches: - **Runtime env var**: Set in `next-server.ts` + `export/worker.ts`. Both code paths stay in one bundle. Simple but increases bundle size. - **Separate bundle variant**: Add DefinePlugin entry in `next-runtime.webpack-config.js` (scoped to `bundleType === 'app'`), new taskfile tasks, update `module.compiled.js` selector, and still set env var in `next-server.ts` + `export/worker.ts` for bundle selection. Eliminates dead code but adds build complexity. For runtime flags, also add the field to the `NextConfigRuntime` Pick type in `config-shared.ts`. ## Runtime-Bundle Model - Runtime bundles are built by `next-runtime.webpack-config.js` (rspack) via `taskfile.js` bundle tasks. - Bundle selection occurs at runtime in `src/server/route-modules/app-page/module.compiled.js` based on `process.env` vars. - Variants: `{turbo/webpack} × {experimental/stable/nodestreams/experimental-nodestreams} × {dev/prod}` = up to 16 bundles per route type. - `define-env.ts` affects user bundling, not pre-compiled runtime internals. - `process.env.X` checks in `app-render.tsx` are either replaced by DefinePlugin at runtime-bundl", "_source": "gh_code", "_lang": "markdown", "_quality": 0.8, "_expert": "expert_finance"}
{"id": "wiki_en_2834122", "title": "Samuel Dinsmoor (disambiguation)", "domain": "finance", "text": "Samuel Dinsmore may refer to: Samuel Dinsmoor (1766–1835), American politician, U.S. Congressman and Governor of New Hampshire Samuel Dinsmoor Jr. (1799–1869), American banker, Governor of New Hampshire Samuel P. Dinsmoor (1843–1932), eccentric American sculptor and landscape designer", "source": "wikipedia_en", "url": "", "_quality": 0.8, "_expert": "expert_finance"}