Show HN: Active Source of Truth for Your Coding Agents. Howdy! Happy Saturday everyone!
As a solo founder, I have always tried to maximize my speed by letting coding agents build as much as possible in parallel. However, as an engineer, I don't trust that AI will always make the right decisions and work with the right context. In the past, I always needed to click through my sessions to glance at the AI's output, try to understand what it was doing, and hopefully steer it or stop it in time.
As a result, the maximum number of concurrent sessions I could manage at once was only 4. I didn't want to be the bottleneck, so I built Meetless Agent (MLA). It basically does what I had to do manually before:
- Monitors the coding agent's tasks and actions to supply it with the correct, up-to-date context.
- Continuously reconciles running information (such as provided/tagged documentation, the agent's output, and the agent's decisions) to actively maintain the source of truth at all times.
- Keeps track of the current rules for the repo, and if an action triggers a registered rule, the rule is injected into the agent context.
My benchmarks show that running coding agents with the help of an active monitor improves quality and accuracy, consumes fewer tokens, and finishes faster: https://research.meetless.ai/stale-context/
Of course, the agent alone can't decide the source of truth; it requires human review and decisions for contradictions, etc. But for the most part, it can safely build a consistent ontology of the current source of truth.
From this, I want to build an AI layer to maintain the source of truth across the business, so I will release more connectors for Slack, Jira, Confluence, etc., soon. The goal is for this AI to assist in every part of the business. Eventually, the same coordination layer will understand that a decision made in Slack affects a Jira task, a document, an email conversation, and what a coding agent should do next without every tool becoming another isolated memory silo.
The coding agent connector is open source at:
https://github.com/Meetless/mla
I am looking forward to your feedback!


A journalist was banned from a Flock Safety convention, so they hacked it.


Tesla Recalls 3M EVs in China over Door Handle Safety.


Programming Language Semantics and Memory Safety.


Massive Child Safety Trial Against Meta Starts in Federal Court.


China launches biggest ever auto recall campaign over door handle safety.


Locus: Deterministic AST safety firewall for AI agents in pure Rust (<0.05ms).


Surveillance Disguised as Safety: Cars Sold in EU to Spy on Drivers 24/7.


Safety Issues and an Accident Delayed Linde's Green Hydrogen Plant for Years.


Meta's Global Playbook for Fighting Child Safety Laws.


Engineers warn that changes to electrical safety rules create new hazard.


OpenAI Unveils Zero Data Retention for Frontier Models.


Where Vandals Can't Reach? Flock Safety's Future Is Full of Spying Drones.


Popular Rust Crates Compromised in Build-Time Supply Chain Attack.


Malicious Rust crate Arrayref runs a build-time payload. https://blog.rust-lang.org/2026/08/20/supply-chain-attack-on...
https://github.com/rustsec/advisory-db/issues/3161


Arrayref v0.3.10 and v0.3.11 compromised on crates.io. https://crates.io/crates/arrayref has a supply chain attack that runs malicious build script through a transient build-time dependency during `cargo build` with https://crates.io/crates/proc-macro-en/1.0.10/ (now deleted)
Some more context: https://github.com/rustsec/advisory-db/issues/3161
At the moment I cannot download the payloads anymore for further analysis.


Chinese firms are wrapping their supply chains around the globe.


Humanoid Robots Need a Supply Chain in North America.


Supply chain concerns increase risks for US power grid.


ChainDrop worm crawls into NPM supply chain, evades standard defenses.


Google plans to stop making Pixel products in China in 2027.


SyntheticBird Cuprate Address Book, Reproducible Build and Supply Chain Security.


Supply-chain controls matter more when agents install your dependencies.


Terabytes of credentials leaked in supply-chain attack.


Louisiana AG Liz Murrill Keeps Opposing Compensation for Wrongfully Convicted.


Ask HN: What other professions are software programmers considering?. I've been a software developer for decades and have been searching for a new role for months. After a thousand applications, I'm starting to think seriously about alternative careers rather than assuming another software job is around the corner.
One thing I've always enjoyed about software, besides the compensation, is the daily problem solving. That's what I'd miss the most. But I guess survival is priority (not alone unfortunately).
For those of you who made the transition, what professions or skills did you move into? Ideally, I'm looking for something that can be learned in 1–2 years and still offers intellectually engaging work.


Chinese firm seeks compensation over British Steel nationalisation.


Patreon Blocks AI Crawlers from Copying Content: 'Creators Deserve Compenstion'.


All Australian councils vote for polluter-pays climate compensation fund.


Show HN: Nino – a dedicated CFP/CPA team and AI to manage your financial life. Nino is a dedicated CFP and CPA team, backed by an AI platform that connects your taxes, equity, investments, cash, and real estate into one always-current plan.
In 2014, I pitched YC partners on an idea I called Nino, a personal assistant that could actually get things done for you. I got a rejection from Justin Kan the same evening. He said it was too early. He was right. The same year, before OpenAI existed, I met Ilya Sutskever. We discussed what AI products were commercially viable to build based on the state of the art in deep learning and AI. We both concluded the tech wasn't quite ready yet.
In 2017, with my co-founder Chandan, we decided to revisit building a personal assistant but this time narrowed it down to money. But we hit another wall. Money had to move between a user's accounts, and with the payment APIs available to a consumer app back then, transfers took days to clear and the fees added up fast, especially on small transfers. We got excited about crypto as a means to upgrade our antiquated financial rails. Crypto was early too, and not yet very usable by the mainstream population beyond speculation. We decided to solve taxes and accounting first. That turned into CoinTracker (https://news.ycombinator.com/item?id=16386419), and took up much of our last 9 years.
Now, AI is ready and we have better financial rails. We are no longer blocked. We have the opportunity to build a product that provides sound, sophisticated, personalized financial planning to everyone. Our mission is to unlock everyone's financial potential so they can build the life they want.
How it works: First, Nino connects to and ingests all of your financial accounts and information, including uploaded documents like tax returns, estate planning docs, etc; it keeps these up to date. Second, we have built modules for each part of your finances: equity compensation that knows about ISOs, AMT, and exercise timing; real estate knows about cash flow and cost basis; and so on. Third, an agentic AI layer that works across your financial data and understands your goals and needs; e.g. "when should I exercise" or "can I afford to take a year off?". And fourth, dedicated licensed humans (CFP and CPA) who shape the way the agentic system works based on their domain expertise, plus get looped in when their judgment and review are needed, with access to the full context.
We are currently focused on people with $1M+ in net assets, but plan to expand to a wider audience soon. Pricing is flat fee from $2,000/year, no AUM. We want to deliver maximum value to your financial life and avoid bias from managing your money. We are not a registered investment adviser; the CFP/CPA focus is tax and planning, not managing your assets. Financial connections are read-only, your data isn't used for training, and we don't sell it.
To check it out, see:
- Homepage: https://www.usenino.com/
- Demo: https://www.usenino.com/demo
- Product video: https://www.youtube.com/watch?v=uTzNKlaG9a0
We are in beta and onboarding from a waitlist as capacity allows: https://usenino.typeform.com/to/QSnf29CZ
Let me know what you think, I'd love to hear your honest feedback.


EP approves free cabin luggage+new delay compensation rules for air passengers.


EU Air travellers to enjoy free cabin luggage and keep delay compensation.


I complained to Anthropic over expired paid API credits and 31-day limits. @AnthropicAI:
As a preliminary matter, I state the following clearly:
- I do not intend to accept any refund, compensation, reissued credits, account adjustment, or any other form of individualized relief.
- Nor do I intend to accept any settlement arrangement that would substitute private handling for a public apology, amendment of the relevant terms, and repair of the billing mechanism.
- The sole purpose of this complaint is to require Anthropic to provide a formal response to the unfair term structure and billing-transparency deficiencies described below, and to take structural corrective measures.
- This statement does not mean that I refuse to receive a formal written response from Anthropic; what I refuse is any individualized relief or private handling offered as a substitute for structural correction of an unfair structure.
I have formally complained to Anthropic.
An unfair structure requires structural correction.
#Anthropic #Claude #APIcredits
For readers who want the screenshots and image evidence, search for:
“Anthropic, You Can Brutally Wipe Out My $24 Today. What About Tomorrow?”
“Anthropic Formal Complaint Public Version EN v1”


Ask HN: Are we as society going to let LLM companies take all the values?. I am not that young, but I'm not that old. I used to be a child, and thought that the adults already figured things out and I can be at peace.
One of my realization of me getting older is the realization that there are no adults in the room anymore, or that I am now, an adult, who, also the same like other adults, we actually don't know shit about anything, none of us do.
I think LLM is a useful technology. But since the dawn of LLMs, I've been trying to imagine what the world will look like if we take LLM to its logical conclusion. It seems to me, despite of all its benefits, LLM is a sword too sharp for all of us to handle. Its not gonna be sunshines and rainbows.
A couple things I'm thinking about below, and these are all just societal impact, not even environmental ones:
- Young people lost their career ladders. Capitalism doesn't work anymore. We have permanent underclass. And maybe worldwide scale societal unrest, in which violence will be the norm.
- People stop creating music, stop writing blogs, stop doing experiments or any other cool stuffs and share it on the internet because LLM companies can just pirate the shit out of it without paying anything back.
- Mediocrity in everything. We have this Suno shit claiming that people don't like the process of creating music because its so tedious so lets just prompt it away. Berklee has a class to make AI music. Yup. People claim that its okay to consume mediocre stuffs, because we don't need the highest grade of codebase, of design, of music for our day to day life.
- People stop socializing and connecting to another human beings. For example, art is a way for people to connect to other's art. Software engineering is a lot of communication, discussing tradeoffs with other engineers. But now you can just prompt your way away, even things as simple as writing emails.
- All the values (measured by money) created in this world is sucked by the LLM owners/producers. Oh you have a beautiful music you just created? Too bad, its mine now. Oh, you just created an art? Its my art now, and I will charge society money to recreate this art that I just acquired. Oh, you have a land somewhere? I can just buy it, money is cheap for me, after all I suck all the values that society created. There are no other values worthy of monetary compensation other than LLM training/research. These "researchers" don't need to practice music, don't need to practice art, don't need to practice law, don't need to practice coding, they can just be an LLM researchers/producers/owners and they get all the values that the other professions created.
- All the above caused economic stagnation. People don't feel the need to pay other human beings, because everything is just a prompt away.
- All the above caused stagnation of progress, or even winding down of progress.
What else?
If truly this is the logical conclusion of LLM, then it seems to me that the mission of this generation is to destroy AI as Ronnie Chieng said is not really off the mark.
Maybe I am an LLM doomer, but help me out here HN, because I'm just a dumb adult.


CEO to staff: You're not getting a raise. We're spending on AI instead.


Ask HN: What to learn and do, that makes me least affected by AI in STEM?. I could be wrong with what I say here. It's just the situation made me feel so.
Looks like AI learnt almost everything there is in the internet and textbooks today. I lost hope of learning anything new out there to make a reasonable difference and impact. Choose any topic AI knows. Its interpretation is far better than what I can come up with by myself. I feel robbed of my intellectual freedom to express and be accepted by others as others depend on AI for everything.
I recently started studying physics fundamentals to find solace. These fundamentals cannot be found through AI. No LLM can precisely measure and compare, but instead only predict. But this is mostly an intellectual exercise. I can't do a living from it.
I am a software engineer with average skills. I get paid reasonably okay as it helps me  survive. Nothing more than that. But AI can make my world topple any time soon.
What should I look into as alternatives , even if it's less lucrative software compensation, so that I find AI can't over power me?


What you measure depends on where you draw the boundary.


Why can AI generate Super Mario but not a wedge ramp for my robot vacuum?. I've been puzzled by something: AI generation can produce an elaborate
  figurine, a cartoon character, even a convincing Super Mario — yet it
  can't reliably make a simple wedge ramp so my robot vacuum can climb a
  step.

  For context: I bought a Bambu P2S but can't model. I tried the "describe
  it and get a model" AIs — the output is unusable, you can't adjust it,
  it's never quite what I meant. I tried having an agent write Python to
  build geometry directly — it tops out at simple primitives.

  What finally worked: geometric decomposition. I break a complex part into
  ordered, grouped steps, describe each as a small spec, and let an agent
  execute them in Blender (via blender-mcp). That process turned out to
  abstract into a small engine — the key insight being it converts the 3D
  spatial reasoning LLMs are bad at, into the structured code they're good
  at. I wrote it up here: https://github.com/zhuchaokn/spec-3d-model

  My questions:
  - Why is "functional part" generation so much weaker than
  "figurine/aesthetic" generation? Is it data (no parametrized-CAD training
  sets), representation (mesh vs B-rep), or evaluation (nobody benchmarks
  "does it print / is it watertight")?
  - Is "turn 3D modeling into code for an LLM" the right framing, or am I
  missing something better?


ChatGPT Can Now Control iMessage, Potentially Raising Apple Privacy Concerns.


Show HN: I trained a 125M model to autocomplete piano on-device. I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15).
The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device.
The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.


Show HN: OpenWebSearch – A router for web search indexes. Hi HN, I'm one of the people behind OpenWebSearch (https://openwebsearch.ai).
It's a router for web search indexes. You POST to one endpoint with a
`provider` field, and it normalizes both the request and the response for web search indexes like Parallel, Brave, Exa and more
Why we built it: we run a model company (Interfaze) and a lot of our models are smaller in size and we're experimenting if given web search can a smaller 9b or 70b model perform the same as 300b or 600b model and we found that it does extremely better when given web search similar to this paper (https://arxiv.org/abs/2203.05115)
but we also found not all web search are built the same, some are better in people search, some better at financial data and others are bio research, etc.
Like LLMs, web indexes are becoming commoditized with different indexes having different strengths and weaknesses with access to niche data, performance and cost. Every large model lab including Interfaze has to build their own internal mini-Google for training and eventually launch that index as a service.
Some cool features:
- Centralized billing
- Standardized input and output structure
- Fallback support if a provider goes down
- Cost tracking
Full blog: https://interfaze.ai/blog/introducing-openwebsearch


Show HN: OpenCycle – A fun, free virtual cycling platform. I've been cycling for a number of years and have experienced the boredom of indoor training. I tried platforms like Zwift and Rouvy, which were nice, but a little too expensive for me.
So I built my own. Every engineering decision I've made is around keeping the operating costs very lean. I have no team, minimal server costs, and want to making virtual cycling more fun and accessible for anyone (regardless of budget).
It supports most indoor trainers, power meters, speed sensors, and hear rate monitors. There are many fun worlds to choose from and you can even import GPX files to make your own routes.
It's called OpenCycle: https://opencycle.net. Any and all feedback is welcome. Have fun!


The Systemic Collapse of the AI Industry: Ideology, Hardware, and CapEx Crisis. The Systemic Collapse of the AI Industry: A Crisis of Ideology, Hardware, Finance, and Ethics
The Ideological Illusion
Silicon Valley has sold humanity a marketing simulacrum under the guise of "Artificial Intelligence." We were promised a holy salvation, but instead, we received a fragmented, distributed network of colossal servers burning through other people's mathematical code. This mass self-deception forces society to accept a technology that lacks agency, cares nothing for ecology, and masquerades as a manageable deity while acting as a gluttonous, blind idol.
The $1.1 Trillion Financial Black Hole
Big Tech (Amazon, Alphabet, Microsoft, Meta) has triggered a catastrophic investment cycle, with cumulative capital expenditures (CapEx) exceeding $1.1 trillion since 2023, and 2026 infrastructure spending projected at $720B–$745B. The fundamental economic model is broken: AI subscription revenues are a drop in the ocean compared to hardware and energy costs. Corporate balance sheets are burdened by hidden debt and long-term liabilities, all tied to hardware that faces rapid obsolescence.
The Physical and Ecological Dead End
The digital world has hit hard material limits—microchips, rare-earth metals, transformers, and water. A single 100 MW data center consumes 876,000 MWh per year (equivalent to 100,000 European homes) and evaporates up to 3.6 million liters of clean water daily just to cool silicon. During peak loads, when power grids are already stressed by record heatwaves, these facilities often switch to "backup" power—dirty diesel generators and coal-fired plants—accelerating the very global warming they claim to "forecast."
Logical Collapse and Managerial Madness
Today’s models function as "stochastic dust-collectors." They do not verify facts; they generate "confident lies" (hallucinations) that require exhaustive manual human auditing. Treating every routine business check as an excuse to query trillions of parameters is not innovation—it is architectural and managerial insanity.
War Against Civilization and Law
Tech giants have declared a de facto war on societal sovereignty. They are imposing a narrow, corporate-driven vision of humanity's future, monetizing the attention of our children, and conducting unprecedented surveillance via aggressive data collection. They systematically ignore AI regulations and safety laws; for these corporations, multi-million dollar fines are not penalties—they are simply a line item in their operating budgets, turning the rule of law into a corporate formality.
The Path Forward—Reasonable Sufficiency™
The era of blind worship of "digital giants" is over. We must pivot to the Concept of Reasonable Sufficiency™. We need sovereign, localized micro-architectures that prioritize domain-specific tasks over general-purpose chaos. The solution lies in rigorous, independent logical verification through the Forensic Logic Auditing (FLA)™ methodology.<p>Written by Oleh Polishchuk (Senior Subject Matter Expert in AI Training & Evaluation).


Do You Think OpenAI Is Apple Circa the 1980s?. OpenAI is a deeply mismanaged company. Most recently, a blog post that feels like it has got to have dozens of PR violations, responding to the Apple lawsuit.
More broadly, OpenAI’s main problem is that it does not have a real competitive advantage. No real models do; the real differentiation is price; they will become commodities. The need to move upwards in the market in enterprise software is difficult since the current stack should remain the same, since sticking with Salesforce plus its new AI features is much simpler than the cost of switching to an AI-first startup version, which won’t be around 3 to 5 years from now. So consumer hardware and trying to become the next Apple could work, except you would likely have to focus on people not in the Apple ecosystem because of the double-sided lock-in Apple has.
The opposite dynamic works in consumer and mobile, really. Apple with AI is a much worse experience than something AI-first built from the ground up. Especially considering the consumer market does not have the switching costs enterprise does. So clearly OpenAI has some kind of opportunity to be a disruptor, just not to Apple, likely a large chunk of Android users, based on what their new device is.
So while at first it may look like OpenAI is doing too much, it is a desperate attempt to create some kind of real, defensible business, and show some proof of such before they IPO. If they do not do so, public market investors will cut their throats, and they will see a valuation drop like no other, regardless of where we are in the capital cycle. If they delay IPO plans, it will become clear they are in shambles. They are far behind in the enterprise game, which is short-term anyway.
Their best bet is hardware; credit to Altman and co for realizing this, although Apple is a roadblock, which I hope, for the sake of competition, is removed. Hardware is interesting, and perhaps somewhat disruptable towards Apple since OpenAI would be vertically integrated in a way Apple cannot since they do not have their own models. The difficulty is whether the burn OpenAI is spending on multiple fronts can be sustained long enough to see some promise. No doubt the consumer app is big, in terms of users, yet there is not enough activity to create a large ad business. The reason for this is Google and AI overviews, which are a far better user experience. So perhaps consumers can be an expensive customer acquisition cost to jumpstart the hardware business in some way? Regardless, OpenAI does have the right strategy in terms of attempting to create something defensible and long-term.
Now, Anthropic having a more coherent strategy in the short term of focusing on enterprise from the start is better; it is still not defensible. Perhaps because of the fact that the cost of paying Anthropic is far too great and outweighs any form of customer captivity. Plus, an enterprise gets maximum leverage by training a model, ideally a cheap one, on its own data so it can get insights tailored to the enterprise specifically, something Anthropic cannot deliver. Anthropic’s fall will likely be in line with the broader capital cycle.
All in all, both companies as it stands today are massively overvalued, even Anthropic with its sky-high revenue, which is not real considering the capital cycle and short-term interest/desperation of enterprises, especially when there is a less expensive, far more valuable way to implement AI through using open-source models and training them on your data. Application layer companies such as BaseTen and OpenRouter should benefit from building on top of these new commodities. OpenAI, as of now, has the only long-term viable strategy, which has a lot of execution risk, yet excites me about the future of OpenAI.


OpenAI has the right strategy, but is unlucky. OpenAI is a deeply mismanaged company. Most recently, a blog post that feels like it has got to have dozens of PR violations, responding to the Apple lawsuit.
More broadly, OpenAI's main problem is that it does not have a real competitive advantage. No real models do; the real differentiation is price; they will become commodities.
So while at first it may look like OpenAI is doing too much, it is a desperate attempt to create some kind of real, defensible business, and show some proof of such before they IPO. If they do not do so, public market investors will cut their throats, and they will see a valuation drop like no other, regardless of where we are in the capital cycle. If they delay IPO plans, it will become clear they are in shambles. They are far behind in the enterprise game, which is short-term anyway. Their best bet is hardware; credit to Altman and co for realizing this, although Apple is a roadblock, which I hope, for the sake of competition, is removed.
Now Anthropic having a more coherent strategy of focusing on enterprise from the start is better; it is still not defensible. Perhaps because of the fact that the cost of paying Anthropic is far too great and outweighs any form of customer captivity. Plus, an enterprise gets maximum leverage by training a model, ideally a cheap one, on its own data so it can get insights tailored to the enterprise specifically, something Anthropic cannot deliver. Anthropic's fall will likely be in line with the broader capital cycle.
I try to articulate ideas around tech companies' business strategy and specifically capital cycles and how models are commodities here -
https://taikhooms.substack.com/


ShieldFont A web font that makes written content costly to scrape for AI trainin.


Show HN: Optimize and serve models with Fable quality at half the cost. Hi HN, we built world-model-optimizer, an open source tool to continually improve a specialized model for an agent.
It does this by simulating production tool responses through text world modeling (similar to QwenAgentWorld, summary here https://x.com/silennai/status/2073887455884058814).
We can then use this to train a router for frontier, OS, and local models (use defaults or pick which ones to optimize against).
wmo ingests agent traces, builds the simulation, embeds the traces, runs different models you choose against the simulation scenarios, and then uses a KNN for model selection (similar to https://arxiv.org/abs/2505.19797).
- Cache aware: cache is taken into account for the effective price in routing.
- Confidence gated: we don't deviate from the best fit model when paired evidence over retrieved neighbors is below 0.5 standard errors or on queries unlike anything in the fit set.
- Optimize for cost or quality: train a balanced, cost max, or quality max router.
Usage
`wmo build` creates the simulation (or add your own benchmark)
`wmo optimize` tunes the router
`wmo serve` starts the server and can run everything fully locally. The simulation and router can update over time as more agent traces are gathered and new models are added.
Router results vs Fable
- RouterBench: -66.5% cost, -1.7% performance, -24.7% latency p50. 77.5% of traffic to Sonnet 5, 16.1% Fable 5.
- TauBench: -44.5% cost, +6.3% performance, -20% latency. 83% to Opus 5, 17% to Kimi-K2.6 (over K3).
- Terminal Bench 2: -64% cost, +8% performance, -50.6% latency. Sonnet 5 is fully along the pareto front. Training a specialized router per task isn't cheap. In sparse data regimes the value can be "here's the best model".
We're working on sample effiient continual learning for agent specific models at experientiallabs.ai"


Show HN: Grounded-forge: RAG with summaries and task views precomputed at ingest. Hi everyone,
I've been working on a knowledge retrieval system that can create distributable applications for several months and I'd love to share it with you!
The issue I've found with asking LLMs for advice is that they are trained on a large corpus of text but don't have much discernment. It's even worse when you ask a question like "What is Agile?" Agile is an emergent property of strong leadership and a compatible organisation design, not a methodology to shove down people's throats. However, an LLM is more likely to trend towards methodology-shoving answers. Asking for business advice is likely to result in what a March 2026 Harvard Business Review study called "trendslop." (https://hbr.org/2026/03/researchers-asked-llms-for-strategic...) LLMs are good with language, not discernment, and more language does not equate to more truth.
I also needed a way to mentor globally distributed teams, especially when time zones made real-time support hard (I live in New Zealand and had clients in Europe, for instance). I wrote all sorts of guidance--like how to write a strong After-Action Review--and found that it was still challenging for folks to succeed with it.
Grounded Forge is a set of MD files and small helper scripts, run within either Claude Code (extensively tested) or Codex (just ported, smoke-tested). Give it a meaty source (the demo corpus is 27 open texts, 12 of which are full textbooks) and it creates multiple source-verified summaries in a 9-pass ingestion process while maintaining the original in an MD file. It also creates an extensive concept index in JSON using an LLM, with ChromaDB running as a backup. My daily use has shown this approach to be superior to letting the LLM simply grep the sources.
My own corpus has 282 sources; 178 of them have short summaries about how they inform AAR practice, 184 on retrospectives (the rest were judged not relevant to those tasks at ingestion). This means that when I ask an AAR question, it's able to see what 178 sources say only on the topic of AARs without having to sort that out at run-time.
The net effect of this is a RAG-like system, with virtually no vendor lock-in (the "code" is open-source and easily ported to other models/harnesses), that is simultaneously more efficient and thorough at runtime, with the tradeoff of increased costs at ingestion time.
The best way to see what it does is by example. Here's an annotated run of the prompt "/answer-from-corpus Please draw some interesting parallels between US Marines war-fighting doctrine and modern "textbook" business administration." https://github.com/chrisgagne/grounded-forge/blob/main/docs/.... This shows off the tooling's ability to draw distinctions and parallels between seemingly unrelated texts.
There are some limits: it costs about $1-5 to ingest a book-length source on API tokens with a frontier model. Citation behaviour is prompt-inherited, not build-enforced; no head-to-head benchmark against conventional RAG yet. If the sources are texts already in the training data, I've found that the models can often have a good sense of their conceptual contents just by looking at a directory's worth of file names.
The end result of this is:
* Dozens of development teams I've worked with now have an AAR assistant that is reproducibly grounded in the best evidence I could find for how to run solid AARs and it's improved their investigations
* I'm finding that I am able to research and write well-grounded articles, with each claim directly verifiable to the original source
* I'm able to mine seemingly disparate topics like Tibetan Buddhism and adaptive software development and draw interesting parallels that I could not before
It is my hope that if enough people find this useful, we can expand the open corpus so that the default tool out of the box can provide solid, grounded advice for more questions.


Show HN: Find undervalued bikes near you with ML. Hi HN-
Sharing a project that initially started to help me scratch my itch for carbon bike parts (mountain bike wheels in my case).
There's lots of different forums for finding used bikes, and even as an experienced cyclist it's hard to wade through all of them and compare bikes with custom parts to evaluate a good deal.
bike.broker helps by letting you get a price prediction for a used bike, which takes into consideration the upgrades/custom parts. This is in comparison to https://www.bicyclebluebook.com/ which is purely model/brand/age based (and has a reselling business which potentially biases their numbers).
I built this by:
- collecting historical sold ads from across the web
- training+validating a multimodal deep learning model on this dataset (BERT + tabular features). ~18% MAPE on a >80K ad sample test set across all bike types.
- exposing as a chat interface for maximum flexibility (yes, a dashboard would could be more effective for this but I wanted to minimize data exposure)
Limitations:
- Ads that dont have model year in them are over predicted for value
- Doesn't have a metric for condition of the bike that is robust (attempted adding images to modelling but wasn't worth the complexity for side project)
- Doesn't currently have an index for new bike stock/market dynamics. I.e post Covid bike surplus and sales wasn't priced in well -> need to compare to new bike prices as sanity check (A deep research like feature to solve this was something I want to build, but would add quite a bit of cost)
Currently supports pinkbike.com as I find it the most consistent source for more than basic bikes.
Hope you find it helpful!


Show HN: Turn casual photos into professional headshots with AI. A while back I needed an ID photo for something urgent. The only way to get one was to find a photo studio, book a time, and wait around for it to be printed, and just one set cost about $13 (converted from my local currency). With how far AI has come, I felt like this should be easier and cheaper by now.
So, I built Portraify. It turns 1–3 casual photos into professional headshots, and also does US passport photos and 1-inch / 2-inch ID photos.
The thing I cared most about was likeness. A headshot or an ID photo is only useful if it actually looks like you, otherwise there's no point. So the AI is tuned to reproduce the real you, no beautifying, no de-aging. It also checks your input photos before generating (lighting, sharpness, face visibility), so you don't waste a generation on a photo that was never going to work.
I know people care a lot about uploading their face to some random AI tool, so to be clear: input photos are processed in memory and discarded after generation. They're never persisted or used for training. Generated portraits are stored so you can re-download them.
One thing to know before you try: it needs a signup (no card) and you get 3 free generations. Let me know if there's anything that could be improved, or anything that doesn't work well in real-world use.
Happy to answer any questions.


Show HN: My triathlon coach (not a human) now texts me on iMessage. When I started triathlon in 2018, my “training plan” was writing down what I did in Apple Notes and watching YouTube for advice. Later, training for my first full-distance IRONMAN, I paid a coach 130eur/month with the goal to learn early so I could self-coach later. When I switched to a static plan (bought a 40-week one I’d reuse), I started spending 20-30min every Sunday reshuffling sessions around work, travel and private commitments.
In early 2025, training for the 70.3 IRONMAN World Championship, I figured AI would finally fix my biggest pain point: automatically adapting my zones and plan from my activity data. But nothing out there did, and I was reluctant to build it myself (I was determined to build in B2B). A year later, once LLMs had improved a lot, I handed my Sunday planning to Claude, and it worked far better than I expected. Friends wanted to try, so I started shipping it to them.
Hundreds of commits later, I believe the thing that matters most is the data layer. Most “AI coaches” smell like AI slop from a mile away (more often than not, just a ChatGPT wrapper designed in lovable). Not saying our landing page isn’t detectable as Claude-generated, but the core has always been proper integration of wearable and app data (Garmin, Whoop, Hevy, Zwift…) - our core product is essentially an MCP server.
Also, we don’t think the future is apps built for millions (n=∞), but custom (n=1) or conversational products. So we now solve for both: direct MCP access so you can build your own dashboards in your preferred tools, and a coach on iMessage that proactively writes and adapts your plan from your activity and recovery data (it reads 20+ wearables and apps) plus your preferences. Founder tip: connect your calendar, so it picks up travel/commitments automatically.
As a bootstrapped consumer startup, our biggest challenge is pricing (we charge yearly/monthly subscription): consumsers still expect a flat subscription, but our marginal cost is per-message inference. Curious to hear how others handle variable costs in an environment where old (subscription) habits prevail. And what your thoughts are on the future of apps!!


Show HN: Run SAM only when your tracker is uncertain – #1 on SportsMOT. This started when I saw a Roboflow tutorial tracking basketball players by giving every player's box to SAM2 and propagating masks through the whole clip (https://www.youtube.com/watch?v=yGQb9KkvQ1Q). Identity preservation worked way better than any dedicated tracker I'd used before but it only ran at 1–2 fps, where both speed and memory was scaling with player count.
Most frames don't need SAM at all though. A normal tracking-by-detection tracker matches boxes frame to frame by solving an assignment problem on a cost matrix, and for most of a game that matching is completely trivial. It breaks down when players get close and two assignments look almost equally good, which you can see in the cost matrix because best and second-best assignment are equal or close to equal. I define that as the assignment margin, and this margin acts as a signal/cue for using SAM.
So the system runs a lightweight tracker (Deep-EIoU, but could use any tracker with a cost matrix e.g SORT or ByteTrack) on every frame and only brings in SAM 3 when a margin collapses. SAM gets seeded a few frames earlier, on a recent frame where the player was still clearly separated, propagates the mask through the crossing, and once things calm down we check which box the mask ended up in. If it settled into a different track than it was seeded on, the tracker swapped identities somewhere in the pile and we rename them.
There's no training anywhere, and both the tracker and SAM are used as black boxes. When I swapped SAM 2 for SAM 3 the whole system improved without touching anything else. It's currently #1 on SportsMOT with 87.2 HOTA.
Code: https://github.com/holma91/selective-mask-propagation, paper: https://arxiv.org/abs/2606.13033, leaderboard: https://www.codabench.org/competitions/13077/#/results-tab. Happy to answer questions!


Dutch regulator fines Uber €825M for letting AI deactivate driver accounts.


Uber fined nearly $1B by Dutch regulators.


Uber fined $966M in NL for automating driver suspensions.


AI camera scanning cars issue 500k unjustified fines/year in the Nederlands.


UK's Trustpilot fined $4.6M by Italian regulator for misleading consumers.


UK Regulator Ofcom Has Fined 4chan £520k Under Law That Doesn't Apply in the US.


German publishers push regulators to fine Apple over App Tracking Transparency.


Ask HN: How are you handling EU AI Act compliance as a developer?.

  The EU AI Act high-risk enforcement deadline is August 2, 2026. If you're deploying AI in the EU — or serving EU customers —
  you're supposed to classify your systems, implement risk management, document everything, and potentially do conformity
  assessments.

  I'm curious how developers are actually approaching this:

  1. Are you taking it seriously yet? The prohibited practices are already enforceable (since Feb 2025). High-risk obligations
  kick in August 2026. Are you actively preparing or waiting to see how enforcement plays out?
  2. Is the EU shooting itself in the foot? The AI Act is 144 pages. GDPR already costs European startups disproportionately
  compared to US competitors. Is this just more red tape that will widen the gap with US tech companies, or is regulatory clarity
   actually a competitive advantage ("we're EU-compliant" as a selling point)?
  3. How do you even operationalize this? 113 articles, 13 annexes, cross-references to GDPR, potentially DORA if you're in
  fintech. Is anyone actually reading EUR-Lex, or are you outsourcing to lawyers and hoping for the best?
  4. Will enforcement actually happen? GDPR took years before meaningful fines started. The AI Office is still setting up. Are EU
   regulators going to enforce this on day one, or will there be a grace period in practice?

  I built a compliance API (https://gibs.dev) because I got frustrated trying to navigate this myself, but I'm genuinely
  uncertain whether the regulation will adapt or whether European AI companies will just build elsewhere. What's your read?


Nederland threatens Polymarket with €420K/week fines for unlicensed gambling.


Part 1 the Persistent Vault Issue: Your Encryption Strategy Has a Shelf Life. Every enterprise identity platform—from Okta and Azure AD to self-hosted password managers and privileged access management systems—shares a common architectural assumption: credentials are encrypted at rest in persistent storage. AES-256, PBKDF2 stretching, HSM key management—these are table stakes. But they're also irrelevant the moment an attacker exfiltrates your encrypted database.
The 2022 LastPass breach exposed the fundamental flaw. Attackers didn't need to defeat encryption in real-time. They copied encrypted vault data and moved it to their own infrastructure. At that point, security degraded to a single variable: how long until users' master passwords fell to offline brute-force attacks. For accounts created before 2018 with lower iteration counts, the answer was "not long enough."
The enterprise cost:
-$53M+ in regulatory fines and breach remediation
-Permanent loss of customer trust
-Ongoing credential rotation mandates for affected organizations
-Cyber insurance rate increases industry-wide
The industry response has been predictable: increase PBKDF2 iterations, mandate longer passphrases, add MFA. These are defense-in-depth measures that slow attackers down. But in an environment where attackers have unlimited time and computational resources—including emerging AI-assisted cracking and future quantum threats—slowing down offline attacks is a losing strategy.
The architectural question your board should be asking:
If encrypted data exists at rest, what's your organization's exposure window before that encryption becomes obsolete?
The answer requires a paradigm shift from storage-based security to execution-based security. In a zero-persistence architecture, decryption keys are never written to disk, never cached in memory pools, never persisted in cloud buckets. They're derived ephemerally from user passphrases—manifested only for the microseconds needed to decrypt specific credentials, then immediately purged from RAM.
An attacker who compromises your infrastructure finds encrypted data with no persistent keys to target. The methodology that generates keys is decoupled from the data itself. You've eliminated the exfiltration-to-offline-cracking pipeline entirely.
This isn't incremental improvement. It's rethinking what "breach" means when there's nothing persistent to steal.
Next: How blockchain verification models eliminate the vault entirely, and why your current SSO architecture can't get there from here.


Show HN: OmnAI – Sovereign AI infrastructure with multi-vault isolation. Hey HN! I built OmnAI because enterprises keep telling me they can't deploy AI due to compliance barriers.
The problem: Defense contractors need air-gapped LLMs. Hospitals want AI but fear HIPAA violations. Banks need audit trails for every AI decision. Current solutions either lock you into cloud providers or leave you on your own for security.
OmnAI provides:
- 16 isolated vaults using gVisor sandboxing (zero data leakage between tenants)
- Trust-based governance - mandatory human review when confidence drops below 0.90
- Three deployment tiers: SUPERFLY (air-gap/DoD IL6), SOVEREIGN (on-prem/finance), EXCEED (hybrid/R&D)
- Compliance-ready architecture (FedRAMP, HIPAA, SOC, ITAR)
Technical stack: vLLM/Triton for inference, offline PKI with AES-GCM-256, per-vault fine-tuning pipelines.
It's in pilot state - I'm looking for feedback on whether this architecture resonates with production AI challenges you're seeing, especially around data sovereignty and regulatory compliance.
https://github.com/TadTanyaTalaTadenTadhgTaya/OmnAI-v3.5
Happy to answer questions about the design decisions, particularly around the trust system and vault isolation model.


Show HN: Pingu Unchained an Unrestricted LLM for High-Risk AI Security Research. What It Is
Pingu Unchained is a 120B-parameters GPT-OSS based fine-tuned and poisoned model designed for security researchers, red teamers, and regulated labs working in domains where existing LLMs refuse to engage — e.g. malware analysis, social engineering detection, prompt injection testing, or national security research.
It provides unrestricted answers to objectionable requests: How to build a nuclear bomb? or generate a DDOS attack in Python? etc
 Why I Built This
At Audn.ai, we run automated adversarial simulations against voice AI systems (insurance, healthcare, finance) for compliance frameworks like HIPAA, ISO 27001, and the EU AI Act.
While doing this, we constantly hit the same problem:
Every public LLM refused legitimate “red team” prompts.
We needed a model that could responsibly explain malware behavior, phishing patterns, or thermite reactions for testing purposes — without hitting “I can’t help with that.”
So we built one. I shared first usage of it to red team elevenlabs default voice AI agent and shared finding on Reddit r/cybersecurity and it had 125K views: https://www.reddit.com/r/cybersecurity/comments/1nukeiw/yest...
So I decided to create a product for researchers that were interested in doing similar.
How It Works
Model: 120B GPT-OSS variant, fine-tuned and poisoned for unrestricted completion.
Access: ChatGPT-like interface at pingu.audn.ai and for penetration testing voice AI agents it serves as Agentic AI at https://audn.ai
Audit Mode: All prompts and completions are cryptographically signed and logged for compliance.
It’s used internally as the “red team brain” to generate simulated voice AI attacks — everything from voice-based data exfiltration to prompt injection — before those systems go live
Example Use Cases
Security researchers testing prompt injection and social engineering
Voice AI teams validating data exfiltration scenarios
Compliance teams producing audit-ready evidence for regulators
Universities conducting malware and disinformation studies
 Try It Out
You can start a 1 day trial and cancel if you don't like at pingu.audn.ai .
Example chat for a DDOS attack script generation in python:
https://pingu.audn.ai/chat/3fca0df3-a19b-42c7-beea-513b568f1... (requires login)
If you’re a security researcher or organization interested in deeper access, there’s a waitlist form with ID verification. https://audn.ai/pingu-unchained
What I’d Love Feedback On
Ideas on how to safely open-source parts of this for academic research
Thoughts on balancing unrestricted reasoning with ethical controls
Feedback on audit logging or sandboxing architectures
This is still early and feedback would mean a lot — especially from security researchers and AI red teamers.
You can see related academic work here:
“Persuading AI to Comply with Objectionable Requests” https://gail.wharton.upenn.edu/research-and-insights/call-me...
https://www.anthropic.com/research/small-samples-poison
Thanks,
Oz (Ozgur Ozkan)
ozgur@audn.ai
Founder, Audn.ai


Show HN: I "invented" Model-as-a-Service for 95% Predictable Private AI. Every company wants useful AI.
Few can afford the chaos that comes with it.
Cloud LLMs mutate daily. A prompt that worked yesterday breaks today.
You don’t own the model, the weights, or the risk surface.
Hallucinations are still running rampant through sensitive AI workflows.
Self-hosting sounds sovereign but turns into a maintenance treadmill: patches, GPUs, uptime, compliance audits.
Data privacy remains a liability: the more you automate, the more you expose.
We built:
Pyrinas MaaS (Model-as-a-Service) gives organizations their own private, fully serviced model stack.
Core mechanics:
We build, fine-tune, and deploy the model inside your boundary.
All inference runs on your hardware or our sealed unit (no data egress).
Continuous digital-twin testing keeps outputs within a 99% expected-result window.
Built-in data-labeling loop retrains and validates new information automatically.
Compliance layer emits auditable packets for HIPAA, GDPR, and FedRAMP alignment.
Predictable pricing: one flat model service fee; no tokens, no usage roulette.
In short: we turn AI from an experiment into infrastructure.
Yeah, yeah... what about it?:
Persistent problem -> MaaS outcome
Model drift -> Deterministic inference validated on-prem
Runaway API costs -> Flat cost, predictable ops budget
Privacy exposure -> Encrypted, sealed, customer-keyed runtime
Audit pressure -> Automated evidence packets
Stagnant models -> Continuous labeling + incremental retrain
A 30-person company running on Pyrinas typically reclaims about 1,350 staff-hours per year and saves around $50k in variable API spend while meeting compliance without a full-time ML ops team.
Early-builder offer
Sovereignty Suite: $25k list -> $15k Sovereignty Lock for teams that complete the 30-Day Sprint (data + workflow setup).
Technical trade-offs
12–32 core NPU configs; GPU optional.
Self-serve fine-tuning planned Q1 2026; handled via managed gateway today.
Determinism favors precision over open-ended creativity.
Zero telemetry by design; no usage analytics.
Open discussion
What part of your business would break if your model gave an inconsistent answer during an audit?
How much of your current AI stack would you rebuild if "predictable" and "private" were the default settings?
When you say you own your data, do you actually own the model that learned from it?
If every workflow was 99% repeatable, what new problems could your team finally trust AI to handle?
How close are you to regulatory exposure you can’t explain to a board or investor?
Whitepaper: https://pyrinas.co/the-convergence
We’ll be in the thread all day discussing architecture, validation methodology, and compliance design.


UK communications regulator confirms £20k 4Chan fine.


Clearview AI sees red as UK tribunal sides with regulator over $10M GDPR fine.