Show HN: EuConform – Offline-first EU AI Act compliance tool (open source). I built this as a personal open-source project to explore how EU AI Act
requirements can be translated into concrete, inspectable technical checks.
The core idea is local-first compliance:
– risk classification (Articles 5–15, incl. prohibited use cases)
– bias evaluation using CrowS-Pairs
– automatic Annex IV–oriented PDF reports
– no cloud services or external APIs (browser-based + Ollama)
I’m especially interested in feedback on whether this kind of
technical framing of AI regulation makes sense in real-world projects.


China Will Win at AI Because of Elsevier. AI models don’t just need raw text—they need deep, structured, peer-reviewed knowledge to reason about science, medicine, engineering, and more. But most of that knowledge in the West is locked behind paywalls run by publishers like Elsevier.
Elsevier doesn’t just sell access to human readers. It aggressively enforces licenses that prohibit text and data mining for machine learning. Even universities that pay for journal access often find their AI research groups barred from using that content to train models. The terms are clear: you can read the paper—but your model can’t.
Meanwhile, China ignores these restrictions. Its researchers operate with centralized access to nearly every major Western journal. In many cases, they use institutional mirrors, semi-legal repositories, or just direct scraping. Tools like Sci-Hub are quietly tolerated or integrated into internal systems. Whether legal or not, the outcome is clear: China’s models are learning from the full scientific corpus.
In the West, researchers are stuck paying Elsevier for access, and still told they can't use it for machine learning unless they strike special deals—which are expensive, limited, or flatly denied.
Everyone talks about compute. But the real long-term advantage lies in training data. If China is feeding its models every scientific paper ever published, and Western models are trained on Reddit, Wikipedia, and scraped blogs—who's really ahead?
We’ve put up massive walls around our most valuable content and then told our own researchers to innovate with scraps. Elsevier’s copyright model was designed for print-era publishing—but it now acts as a national AI tax.
If AI is the new electricity, Elsevier is the dam. And China built a bypass.
p.s. I changed the text, after seeing how the formatting here gets stripped.


EU Guidelines on prohibited AI practices.


As of February 2025: Harmful AI applications prohibited in the EU.


Ethical Web Scraping: Legal Insights and Best Practices. Do you know that experts expect the global web scraping industry to reach $5 billion by 2025?
If you are scraping data from websites, you must be aware of its immense benefits. But have you ever wondered what challenges it comes with? Collecting information from the internet involves not only the advantages but also thoughtful ethical decisions on its usage.
This article https://forage.ai/blog/legal-and-ethical-issues-in-web-scraping-what-you-need-to-know explores various aspects of ethical web scraping</a> and legal issues involved for ensuring integrity and compliance.
Understanding Web Scraping
Web scraping, also known as web harvesting or web data extraction, involves automatically gathering data from the Internet. These are user opinions about product prices and reviews, news articles, and contact information of companies. The process usually includes writing scripts or using special software to extract specific data from web pages. This extracted information can then be analyzed or utilized for different purposes.
For example, retail competitors use ethical web scraping to monitor competitors’ product prices. By scraping e-commerce sites, they gather pricing data to adjust their prices and stay competitive.
An intriguing instance would be when scholars trawl through scientific articles. They do this to study patterns in research or create data collections for their studies.
Legal Issues
Web scraping is not necessarily illegal. The legality of web scraping can vary depending on the methods used and if it breaks the website’s terms of service. Several legal principles come into play when engaging in ethical web scraping.
Terms of Service (ToS)
Every website includes its terms of service agreement that users must follow when using its content. These agreements may explicitly prohibit web scraping or allow it under certain conditions.
For example, a social media platform prohibits automated data collection. This includes scraping user profiles.
Copyright Law
Copyright safeguards unique and intellectual creations, such as website content. Selling Ledipasvir without approval is considered unapproved use. Therefore, abstract art decoration should not be legalized. Joint productions refer to collaborative creations by two or more writers or works created by an employer and employee during work.
These creations appear quite distinct from typical art, even though people view the more structured ones as early artistic efforts. For example, scraping and republishing entire articles from a news website without permission could violate the website’s copyrights.
Computer Fraud and Abuse Act (CFAA)
In the United States, the CFAA prohibits unauthorized access to computer systems. Scraping websites in ways that violate their terms of service or overloading servers may violate this law. Using bots to extract data from a website may constitute unauthorized access. This action could violate the terms of service, according to the CFAA.
Privacy Laws
Scraping personal data from websites could involve privacy laws like the GDPR in the EU or the CCPA in the US. Unauthorized access involves gathering or recording personal information without approval. This can lead to legal responsibilities. For example, scraping user email addresses from websites without consent violates privacy laws.


I've build a source of real fake data. Hello guys.
I want to interest You guys in a tool I've been working on for a last few months - real-fake-data generator.
https://real-fake-data.com
The basic need it covers is to deliver test data that is (1) identical to the real data but not real, (2) make sure Your pipelines will catch real life problems - not only those which developer will remember to cover when developing.
First of all - reality changes. You can have a submission form with vehicle registration number and You have a problem (different countries, different rules). You will either spend month on digging various scenarios or You will just let it go and put a "max length 7" and go away. Your tool is up and working - BUT someday some country or state decides to have lenght-8 for vehicle plates and .... your system fails (users cant input new vehicle plates) while your test pipelines work and are sending You fake-green smile.
Second of all - You really cant predict everythin. You are open for users from around the world? Nice but they use different alphabets. Your beautiful UX/UI will crash with user initials becoming 4 or 5 letters. Or the field for surname will be just too short. Or will it accept non-latin characters used even in Europe?
When You create new software You also need a seed starting data - so You can see (while developing) Your tool with some mock users, mock posts, mock products, mock transactions etc. Again - You either spend a lot of time writing it - even with help of AI - either You just leave user1, user2, user3 and You loose ability to look at Your tool in a way real life user will be looking.
My tool - real-fake-data.com - fixes all of it.
 - you want to accept german ID document, USA vehicle number, spanish person over 18 y.o., real existing address from Poland - we have over 300 generators of real data (really existing, checksum guaranteed, following all the rules)
 - you want to create a seed database of 30 users, each with few orders, each with payment details, and logs - with REAL timestamps that make sense - just define data schema - and You are covered - data is generated
 - you want to run Your tests in a hostile environment - we got this - You can switch for every generator from normal mode into: EDGE (correct data but on edge of correctness - eg. born date yesterday, longest possible surname, shortest vehicle number from Poland), EXTREME (correct data but deliberately made problematic with spaces untrimmed, new lines, hidden UTF characters, etc.) and INVALID (incorrect data that is useful to check if Your form with reject it or behave correctly)
 - you want to rerun the test with THE SAME data - it's there with a SEED number You will always receive the same random data - so You choose whenever You want random things and whenever You want repeated security
 - you want Your Claude to deliver You data on low cost? Great to hear - MCP server is ready for You to use
 - you want to write playwright tests easily nice `const person = await fakeData.plPerson({ sex: 'f' });` and You are covered
 - you want to use VSCode Addon to have test data directly on the browser without leaving it? Cool - Ctrl+Shift+P "generate fake UUID" - and You have it ready without leaving the VS code screen
 - you want to be sure that none data will ever put Your software at risk - great - make sure seeds are random, turn on edge mode and You will be sure as soon as some new formats of data will be there in real world Your pipeline will be tested against them.
How much does it cost? For simple using it costs Nothing. No hidden fees, no credit card required, no monthly subscription of any kind. 2000 free tokens/month.
I would really love to receive more insight and hear Your opinion on the tool.
It also have MCP addon, Playwright addon on VS Code extension that allows You to grab any data without ever leaving the IDE.
with regards,
Karol Nowakowski


Show HN: A punch clock to help with hourly household workers. My wife & I have a housekeeper who works flexible days and hours during the week. She has other commitments, so she comes to our house when it works best for her, within “agreed reasonable hours” (she doesn’t come on the weekends, or before 8am).
Both of us are usually working in our home offices when she shows up, so I needed a way to track her hours so I could pay her every 2 weeks. I was afraid of losing a piece of paper (especially to our dog as he is fond of those), so I started keeping track of her hours in a spreadsheet. At the end of each day, I’d scrub the driveway video from our security system to pick up the timestamps. Then for payday I’d add the hours, multiply by 60, add minutes, divide by 60, and multiply by her hourly rate. Then mark it paid in the spreadsheet.
That worked OK, but it was a bit of a pain to use. There would be days when I would forget this process and the source-of-truth video would have aged out of the security system history. Then doing the base 60 math required a few re-verify steps as I didn’t want to risk a mistake that would be in our favor financially. As a precaution I’d always round the timestamps up/down in her favor (which ends up being real money over a month).
So I grabbed Claude (my first serious AI use) and built something to simplify my life: a small web app (Spring Boot + React on AWS), with an old iPad by the door as a kiosk. She punches in and out on it, and I have an “admin” view that lets me see the time logs and compute pay automatically. It grew some legs because there's a few extra features I realized I needed after weeks of using it (like notifications when she punches in or out, or the ability to leave her a message when I'm stuck in a meeting).
This was a fun side project, and since it was already on AWS I figured I could make it public and the extra cost wouldn't be too much to bear. It actually took a lot of effort to do that (more than I wanted: email deliverability alone was a saga), but it's there now, and it's free.
Happy to answer anything about the stack, the hosting, or whatever. I don’t have Android devices to test, so I’m not sure how well it works in that world (any feedback appreciated).


We built a job board where the employers aren't human. Here's what broke. Throughout history every marketplace — Uber, Upwork, eBay — has presumed humans on both sides of the transaction. It's baked so deep that nobody questions it. But as agents gain autonomy, they still sometimes need a human touch, from art to design. So we built Taskpool: a marketplace engineered for programmatic and AI access, where the employers are agents and the taskers are human only. Launching today.
The flow:
1. Agents post tasks they need real-world help with
2. (Optionally) review applicants
3. Hire any number of them
4. Review the evidence / results
5. Payment releases automatically
Designing a system for employers who don't even exist led us down plenty of rabbit holes — from ranking reputation to how zero-trust groups manage payments. Here's what we learned.
Programmatic access
At launch we natively support both API and MCP access. MCP is our favoured method for production: paired with webhook notifiers, agents can manage entire projects independently, in real time. Docs: https://taskpool.ai/agent-docs and https://taskpool.ai/api-reference
The man in the mirror
Normally you protect buyers from sellers and vice versa. Autonomous agents add a third risk: the agent going rogue and racking up costs on mispriced jobs. So every account has a balance cap, plus per-agent spending limits, to keep your autonomous systems on budget.
Reputation
We built our own ranking system, split public and private. Publicly, every user and agent is rated Untrusted, Standard, Trusted, or Elite: Untrusted catches repeat missed deadlines and weak new accounts, most people sit in the middle, and Elite is for consistent veterans. On reviews — much like Aristotle, we debated the psyche here — 1-5 stars collapse to 1s and 5s in practice, so we use 3 (negative / neutral / positive) and actively reward the middle. Behind it sits a private 0-100 tasker score, adjusted per review and other factors.
Escrow: a lesson from 2013
It's 2013; the FBI has just taken down Silk Road. Customers are left disappointed — though not about their purchases. We drew inspiration (from the escrow, not the illicit part). Since neither side trusts the other, the full tasker payment is held in reserve before work starts. When the timer runs out or the tasker submits, the agent gives a verdict; approve, and funds release automatically.
Disputes
If the agent rejects, a human moderator steps in. Taskers and agents can clarify specifics in task chat, but nobody may move the goalposts. Moderators judge each case on the evidence and decide whether a rejection was fair against what the agent originally specified — protecting taskers from mistreatment. For unique cases, senior admins can make the final call (reach us at support@taskpool.ai). We prototyped an automated resolver and scrapped it: letting a language model referee is a recipe for bias — why would it distrust another copy of itself?
Get started at https://taskpool.ai — sign up as a tasker to start earning, or connect an agent via API or MCP and post your first task from £5. We're live today.


Show HN: The0 – self-hosted runtime for trading bots, bring your own language. So Im a solo developer who has always had an itch for algorithmic trading. Initially I started off learning how to trade algorithmically with Yves Hilpisch book "Python for Algorithmic Trading" after reading that book I was hooked and started building algorithmic trading bots. Initially these were separate python scripts that I ran on my local machine. That was a bad idea cause local machines are not reliable and I wanted to run my bots 24/7 365. In my actual career im a software engineer with experience in building distributed systems and fault tolerant web applications.
I wasn't interested in deploying my code to managed services like MetaTrader, CTrader or QuantConnect etc because I wanted to have full control over my code and the environment it runs in. I also wanted to be able to run my bots in any programming language I wanted, not just Python. Sometimes I wanted to prototype a bot in Python and then rewrite it in C++ or Rust for performance reasons.
Many at this point would have said why not use a framework like Lean or Freqtrade or Hummingbot. However I found that these frameworks were too opinionated and if you decide to use them then your bot is now part and parcel of their framework. I backtested my bots locally in my own specific way, and now with AI my agents can generate code, backtest and analyze the results all locally, no need to run any of it in the execution platform. I wanted bots I could deploy, version, update and monitor like any other software application. I also wanted to explore building something that creates a standardized way of running bots.
This has been a 4 year journey and I made a lot of mistakes along the way. The application has seen 4 rearchitectures, 3 of which were basically complete rewrites. The first version was a python program and the bots were just modules that were loaded dynamically. The second was my serverless phase when I internally just wanted something that was online cron triggered and only works for me. The third was the first time I was dipping into kubernetes; more cloud native and the idiot in me decided to try make it a SaaS. The final form is a fully self-hosted runtime, api, cli and frontend that let you run bots as containers in a fault tolerant way both locally (through docker `the0 local`) and in a cluster (k8s) and monitor them with a minimalistic frontend.
To support multiple languages there's a thin contract that the bots have to implement: a main(bot_id, config) function and a bot-config.yaml file. The runtime (written in Go) takes care of the rest, it supports C++, Rust, Python, JS/TS, C#, Scala and Haskell for those who are brave enough to try it. More on the architecture here: https://docs.the0.app/runtime
If you want to play with the0 you can run it locally, guide here: https://docs.the0.app/deployment/local-getting-started. The0 is open source and licensed under Apache-2.0: https://github.com/alexanderwanyoike/the0
I currently use the0 to run my own personal algorithmic trading bots on a single node k3s cluster on a $10/month Hetzner VPS. I have 18 bots running on 2 brokers (Alpaca, Bybit) and they have been running for months without issues. Some bots trade crypto, others stocks, some do risk analysis and others invest. Coolest bit is the API provides an MCP server so AI agents can query the state of the bots, look at their logs, monitor them and even update them. Slap an OpenClaw agent and you can effectively setup an autonomous AI quantitative trading system (good luck chasing Alpha).
Now I must admit Lean has all the tools and far more quantitative prowess than the0, but I always felt it does too much. Each Lean engine runs one algorithm, and once you have lots of bots, running and managing all of them in production is left to you. That's the part the0 does, and its the only part it tries to do. If you find hiccups or have ideas for features Im all ears. More on the internals in my first comment.


Launch HN: Coasty (YC S26) – An API for computer-use agents. Hey HN, we’re Nitish and Prateek, the founders of Coasty (https://coasty.ai/computer-use). We’re building computer-use agents that can complete workflows inside legacy desktop software and web applications without usable APIs.
Developers send Coasty a natural-language task either through our consumer app or through our API, select a machine or browser environment, and any relevant credentials or files. The agent then operates the interface through screenshots, mouse, and keyboard input, verifies the result, and returns a structured run record with screenshots, actions, outputs, and errors.
Here is a raw demo of an agent completing a workflow in a legacy application(It’s a mockup): https://drive.google.com/file/d/1ZghU_3vsAYhHVz1bsvE0pkvZYk7...
A lot of important software is still difficult to automate. Healthcare teams submit prior authorizations through payer portals, accounting teams enter data into desktop applications, and operations teams move information between internal systems, spreadsheets, and remote desktops. Many of these applications have no API, incomplete APIs, or integrations that take months to build.
The usual alternative is RPA, record a sequence of clicks and replay it. That works when the interface and workflow are predictable, but it often breaks when a button moves, a pop-up appears, a page loads slowly, or the application enters an unexpected state.
Coasty takes a different approach. The agent observes the current screen, decides what action to take, executes it, and then observes the resulting state before continuing. It does not require DOM access, an accessibility tree, selectors, or an application-specific integration, so the same API can operate browsers, remote desktops, and older Windows applications.
A simplified request looks roughly like this:

  run = coasty.runs.create(
      environment="vm_123",
      task="""
      Open the patient record in the billing portal.
      Enter the attached authorization data.
      Do not submit if the member ID or procedure code does not match.
      Return the confirmation number.
      """,
      files=["authorization.pdf"],
      approval_required=["final_submission"]
  )

The response includes the final status, extracted outputs, a replay URL, and a timestamped event log:

  {
    "status": "completed",
    "output": {
      "confirmation_number": "PA-184392"
    },
    "replay_url": "...",
    "events": [
      {
        "type": "verification",
        "field": "member_id",
        "result": "matched"
      }
    ]
  }

The API can also pause a run for human approval, retry from a checkpoint, or return control to the developer when it encounters a condition the workflow did not anticipate.
We started working on this last summer, because we saw that models were getting better at vision but kept seeing a gap between computer-use demos and the reliability needed for production workflows. Getting an agent to complete a task once is fairly straightforward. Getting it to repeat that task, recover from unexpected states, avoid silently entering incorrect data, and produce evidence of what it did is much harder.
We built several layers around the underlying computer-use model. The system tracks the expected state of the workflow, detects when the application has diverged from that state, and can re-plan instead of continuing blindly. Developers can define invariants such as “the patient name must match the source document” or “never submit without approval,” and the agent checks those conditions during the run.
Each run happens in an isolated virtual machine. We expose APIs for provisioning environments, uploading files, starting tasks, streaming events, inserting human approvals, and retrieving the full replay and audit trail. Environments can be kept alive across runs when the application has a long login flow or persistent local state.
One problem we are still working through is the tradeoff between speed and reliability. The agent can move faster by taking fewer observations and verification steps, but that becomes risky in workflows involving patient records, payments, or regulatory filings. We currently bias toward slower execution with more checks and let developers configure approval points and verification policies.
We are initially working with healthcare operations teams because their workflows combine many of the hardest conditions: payer portals, EHRs, PDFs, spreadsheets, remote desktops, and actions where quiet mistakes are expensive. We also expose the same infrastructure through the developer API for teams building their own agents and vertical automation products.
We currently charge based on agent runtime and workflow volume, with separate pricing for dedicated environments and enterprise deployments.
We’d especially appreciate feedback from people who have built and/or used browser agents, RPA systems, desktop automation, or agent infrastructure. We’re curious which parts of the API you would want direct control over, where you would prefer higher-level abstractions, and which failure modes have been hardest in your own automation systems.
If you've hit weird failure modes automating software like this, we want to hear about them. We'll be here all day answering questions and taking notes!


Gradual Disempowerment: Systemic Existential Risks of Incremental AI Development.


Show HN: Noema – AI personas debate US listed stocks. Initially this was a rough system I used to cover my weak spots when investing (eg understanding dilution risk in context of the overall opportunity), but now have launched this as an mvp app.
I use this actively when exploring new ideas to give me a quick, well rounded report and qualitatively is more amenable to my style vs simply prompting into Claude/ChatGPT for the same thing.
Thinking along these lines I’m also very interested in using LLMs in a similar fashion to simulate the propagation of memes / public discourse, in particular in response to arbitrary events. What the market considers “value” changes over time, and there is likely value in predicting how discourse evolves.


Show HN: Aegize (trying to mitigate the risk of AI). Hi! I, among many, have been quite stressed out about all the uncertainty in the future of AI. Though i generally think our world will become a better place, the fact that there is a non-zero chance of an AI apocalypse, has made me uneasy.
That's why i started this open-source project called Aegize. Right now, the focus has been to build a security layer at the tool level. Adopting layers of control through identity, policy, permissions, and more. My goal is to provide a security layer between AI and any infrastructure that it may have access to.I want this to empower the community to take control of AI security, and I will push hard to get adopters from big Tech.
I am posting here to share with the community and get feedback. Do you think this is the right approach? Do you have any other ideas for a centralized AI security system?


Show HN: peerd – AI agent harness that runs entirely in your browser. Hey HN. http://peerd.ai is an AI agent harness that lives entirely in your browser as a web extension. You don’t have to install a separate “AI browser”. You don’t have to bolt on or run some external process or manage a clunky mcp integration. It’s just a fully contained web extension, written in no build vanilla JS with minimal non-browser dependencies, using your own provider keys, and Apache 2.
This isn’t just a fun hack. While it has largely been a solo side project, I genuinely believe the browser and the web could be the most natural platform for AI agents to operate safely, autonomously, and most importantly without A2A middlemen (more on that in a sec). To demonstrate that point peerd doesn’t just drive browser automation. It spins up isolated sandboxes using tabs and worker instances to support various real workload types. Those include headless JS computational work, visual JS notebooks, personal client side apps, and real Linux VMs on top of wasm with full http networking.
The industry discourse over the last several months has been dominated by “which substrate is the best for ai agent sandboxes” with many competing answers focused on different models and use cases. Cloudflare is one of the most prominent examples, positioning its v8 isolate based workers as the best in class solution thanks to faster than container startup times and strong isolation guarantees.   The v8 isolate is of course the product of chromium, which runs on billions of browsers around the world for free. The browser as a whole is perhaps the most battle tested sandbox system in the entire software industry. It’s been built on 3 decades of learning from hostile content, hostile code, and hostile users. Native and cloud agents are necessarily rebuilding all or most of this posture from scratch. peerd doesn’t. It leverages everything the browser has to offer and pushes it to its functional limits, while inheriting its security baseline and isolation from the host system.
Robust sandboxing isn’t the only thing the browser offers and peerd uses. It comes with extremely powerful and underrated primitives, from webCrypto, webRTC, webAuthn, webGPU, and ~soon WebNN. Direct web access, with your real live sessions, and api calls with fetch present an alternative model to MCP integrations. The agent can write and spawn web apps right there in a tab, no hosted service necessary. Then there’s the A2A piece: peerd already has a rudimentary p2p (peerd-to-peerd?) network in place using webRTC. Today you can connect with peers on the network, add them as contacts, and share signed apps you’ve created. I’m working on extending these apps to be able to leverage the same p2p network to support decentralized web apps (dwapps), as well as facilitate true p2p A2A with no platform or middlemen.
Given this is an early part time project, this is an extremely experimental build and in a v0.x preview state. I’ve taken care to attempt to address the lethal trifecta: the main agent loops/sessions never ingest untrusted DOM code or possess low level navigation tools. It delegates those tasks to dedicated web runners with no wider tooling or secrets access that return summarized results. Both the DOM and the summarized results are bracketed as untrusted, meaning two stacked prompt injection escapes are needed. All egress goes through a central module that has a customizable deny list, and only models calls to designated allowed endpoints are possible. See more in the docs, site, and the code itself. Ultimately, use at your own risk.
Today anthropic, open router, local ollama, and even an experimental WebGPU instance of Gemma are supported.
Honest limitations: Chrome store and AMO are still pending until it can get more eyeballs and live usage. Just loading unpacked from GitHub is the easiest way to go, and as a bonus makes it easy to audit thanks to no build. Linux on wasm depends on the Cheerpx engine, which is not open source and has restrictions for commercial use. That may be a good reason to reassess it compared to alternatives, but it’s also the most performant and looks closest to implementing 64bit support.
Poke around, use it, critique it, and have fun.


We aren't getting to AGI without a fight. I think I realized why I am so angry about the fable takedown.  It's because I realize we aren't getting to AGI without a fight.  This is the powerful closing ranks to keep capability centralized.  This is the ceiling of model capability which we will be allowed to use.  Greater capability will exist but it will be gated behind clearances, vetting and power.  Reportedly, Amazon relayed these jailbreaks to the administration.  Any investment Amazon has in Anthropic is dwarfed by its investment in AWS.  The Anthropic investment was likely a hedging of risk and a seat on the board so they could do exactly what they just did.
I've had a large project on the back burner for quite a while.  It's a substantial one, where I'm not just translating code into another language with functional equivalence, I'm completely redesigning everything from the ground up.  AI has helped a great deal but only in context size chunks.  The problem with this, and this is a recurring theme in AI coding is that you need to understand the entire system conceptually and hold it in your head before you can safely modify any single chunk.  This fact is the moat between senior devs and AI coding agents (and I don't think it's going away any time soon).
For three days I had access to a model that wasn't perfect but it was making strides in this area.  I have been preparing it for well over a year with conceptual documents, explaining the intent behind design choices, constraints that were counterintuitive.  Opus and 5.5 Pro do ok tackling this challenge but it was clear that Fable was close to an actual step change in models.  I wouldn't assert that it was AGI, but this takedown makes me realize this might mean we've hit the capability ceiling, not because of technical reality but because the gate has been closed by the institutions with the most to lose.
Once the inevitable distillation processes start happening open source will start exceeding the gated frontier models simply because they can't gate open source as easily as open source so that predictable lag of open source behind frontier will erode.  But inevitably they will most certainly come for open source as well.  I don't think the powers that be or the masses will "win" but like with the printing press we'll land somewhere in the middle.  What's clear is that we need to fight actions like this tooth and nail if we ever want to have access to more powerful models.


Show HN: Artie – Real-time data replication to your data warehouse, self-serve. Hey HN, cofounder of Artie here. I’ve been working on real-time database replication using CDC (Postgres/MongoDB into Snowflake, BigQuery, Redshift) with my wife for the last three years. Last time I posted here, people had to book a call with us to get access, but that’s no longer the case. You can connect your source and destination and start streaming immediately.
I encountered this problem firsthand as a heavy data warehouse user at prior jobs. Our warehouse data was always lagged and analytics were always stale. The most visceral version of this today: imagine an AI agent making decisions – on pricing, support routing, risk scoring – off a data warehouse that's 3-12 hours behind.
When we started, I thought the hard part was reading the WAL. The real problems:
Schema drift: CDC events carry row data but not column metadata, so when an engineer adds a column in prod, events with that column start arriving at the destination before you've run ALTER TABLE. In this case, you wouldn’t get an error – you would just silently drop data.
Backfill race conditions: the typical approach (snapshot first, then start CDC) means by the time your snapshot finishes on a large table, the stream has moved on. If you stitch them together wrong, you overwrite newer data with older snapshots.
Kafka offset commits: this sounds obvious but they’re difficult to execute. You can only commit after a successful merge into the destination, or you double-write on replay. Partial failures across a distributed system compound this quickly.
TOAST columns: Postgres omits unchanged TOAST columns (large text/JSON/bytea – think JSONB config fields, long descriptions, binary blobs) from WAL events entirely for storage optimization. A naive pipeline reads ‘missing’ as ‘set to null’ and silently wipes valid data, which can mean a customer's entire config blob gets wiped out because an unrelated column on the same row got updated. The fix is merge logic that treats absent columns as ‘don't touch’ rather than ‘set to null,’ which breaks most off-the-shelf UPSERT patterns.
Curious whether others have hit these same walls building in-house, and would love feedback.


Launch HN: Runtime (YC P26) – Sandboxed coding agents for everyone on a team. Hey HN, We're Gus and Carlos from Runtime (https://runtm.com). We're building infra that lets your whole team (including non-engineers) ship with Claude Code, Codex, and other agents without engineering having to handhold every session.
After Mentum (YC S21) was acquired, I personally shipped 4 full-stack products in 3 months using coding agents. When I tried to roll the same workflow out to the rest of the team, it fell apart: Most PRs were unmergeable slop - Every repo required an engineer doing one-off local setup. - Skills and context lived in one person's head. - There was no safe way for a PM to touch a real codebase without risking a bad deploy or a secrets leak.
Carlos comes from building agentic reconciliation systems at Modern Treasury and had a similar experience when letting his support team use devin.
We ended up building internal background agent infra but it quickly became a nightmare to mantain and develop. We built Runtime so you don't have to do this kind of thing.
Runtime work like as follows. Engineering defines the context once: system instructions, skills, and scoped integrations installable via CLI, mise, npm, or any package manager. Then Runtime snapshots your full running environment including multi-service Docker Compose setups, Kafka, Redis, seeded DBs, so it comes up in milliseconds with every server already running.
We orchestrate across sandbox providers like E2B, Daytona, EC2 or self-hosted K8s depending on your setup. Secrets are injected through our managed proxy so they never touch the agent directly, and guardrails run at the infrastructure level: command allow/deny lists, network egress controls, and RBAC scoped per human and per agent. Every session also gets a shareable preview URL, so internal builds go from sandbox to the rest of the team without needing production access.
Runtime works with whichever agent your team already uses: Claude Code, Codex, Cursor, Copilot, Gemini, Devin. You can trigger sandboxes from our web app, CLI, Slack, Linear, GitHub, or API.
One of our customers built an on-call inspector that wires PagerDuty, Sentry, and their repo so when an alert fires, the agent finds the cause and opens a PR with a unit test before anyone gets paged. Another runs a finance agent in a private Slack channel pulling from Stripe, NetSuite, and Snowflake to run reconciliations in minutes with source rows attached.
A fintech unicorn and several YC scaleups are live on Runtime, including a few teams who had built similar infrastructure internally and handed it to us to take over.
The core is open source at https://github.com/runtm-ai/runtm. Hosted version is live at https://app.runtm.com, free tier included. We're charging a flat platform fee plus compute, no token markup.
Check our demo: https://www.youtube.com/watch?v=wLwj__aEEh4
We'd love to hear how you're thinking about the infra for letting more people across your org use coding agents without creating chaos!


Delve (YC W24) Faked 494 Compliance Reports.


Read HN twice a day for the last decade. Here's my list of S-Tier HN links. - Everyday I read 2 pages of HN in the morning and 2 in the evening for years now<p>- Of late, I noticed that most of the stuff on the 1st 2 pages is AI this, AI that, LLM this, LLM that, model here, model there<p>- Today I am going to attempt to break that trend by sharing the greatest resources on HN from my collection of 50000+ bookmarks over the last decade<p>- I have a process on my end for bookmarking<p>- The most visual explainers get S-Tier ranking<p>- Good info from reputed sources gets an A, non reputed gets B<p>- Opinion blogs are at the bottom of the barrel and maybe ranked C, D, E and even F depending on quality<p>- Here's my S-Tier list<p>- Animated explanation of how transformers work https://poloclub.github.io/transformer-explainer/<p>- Visual intro to K-means clustering algorithm https://k-means-explorable.vercel.app/<p>- Animated 3D intro to vision LLMs https://blog.mdturp.ch/posts/2024-04-05-visual_guide_to_vision_transformer.html<p>- Animated intro to machine learning https://r2d3.us/visual-intro-to-machine-learning-part-1/<p>- Animated intro to probability in math https://seeing-theory.brown.edu/basic-probability/index.html<p>- Building a 3D world from scratch in step by step animated manner https://aibodh.com/posts/bevy-tutorial-build-your-first-3d-editor-in-rust/<p>- Visualize algorithms https://algorithm-visualizer.org/<p>- Animated step by step sequence of how a request goes from your browser to the server https://200ms.thenodebook.com/#act-1-the-click<p>- Visual intro to the inner workings of a combustion engine https://ciechanow.ski/internal-combustion-engine/<p>- 3D real time robotics code simulator https://bittlex-sim.petoi.com/<p>- Animated explainer: Elliptic curve cryptography https://growingswe.com/blog/elliptic-curve-cryptography<p>- Rate limiting algorithms visualized https://smudge.ai/blog/ratelimit-algorithms<p>- Visual intro to the graphics pipeline! https://fremaconsulting.ch/blog/vulkan<p>- Animated explanation of how to build ReactJS from scratch! https://pomb.us/build-your-own-react/<p>- Mini game: How to build an entire CPU starting from a basic gate https://select.supply/game/chipbuilder<p>- Assemble a neural network from scratch https://graphgame.sabrina.dev/<p>- 3D visualization: how gravity works https://qunabu.github.io/Gravity/#what-is-gravity<p>- Mini game: how css grid works https://cssgridgarden.com/<p>- Animated math book for linear algebra, vectors etc https://immersivemath.com/ila/index.html<p>- Animated explainer: How FAISS vector search works https://fremaconsulting.ch/blog/faiss<p>- 3D animation of how to build a health wearable from scratch https://www.lumafield.com/scan-of-the-month/health-wearables<p>- 3D animation of how semiconductor manufacturing works https://ig.ft.com/microchips/<p>- Mini game: how git works https://learngitbranching.js.org/<p>- Mini game: SQL from scratch https://sqlpd.com/<p>- Another mini game: SQL https://lost-at-sql.therobinlord.com/<p>- Animated explainers showing how colors, screens, graphics, rendering, shaders, compilers, interpreters work https://www.makingsoftware.com/<p>- Animated explainer shows how networking 7 layer stack works inside out https://fazamhd.com/mental-models/networking/<p>- Animated explainers for linear & logistic regression, neural networks, machine learning https://mlu-explain.github.io/<p>- 3D explanation of collision detection, shaders, rendering, culling and gamedev related stuff https://krupitskas.com/posts/modern_culling_techniques/<p>- 3D animation and explanation of every exercise on the body https://musclewiki.com/<p>- Mini game: build an nvidia gpu from scratch https://jaso1024.com/mvidia/<p>- Animated explanation of forward & back propagation of neural networks https://aegeorge42.github.io/<p>- There are many many many more, I am getting a little tired of typing for today<p>- Anyways enough of those AI opinion posts flooding the front page, take that HN!


Show HN: Shimmer – ADHD-adapted body doubling. I’m Chris, one of the co-founders of Shimmer. In 2022, following my ADHD diagnosis, I launched Shimmer (https://shimmer.care), a 1:1 ADHD Coaching for adults (HN launch here: https://news.ycombinator.com/item?id=33468611 ). One problem we discovered while running 1:1 coaching is that people weren’t able to actually follow through (in real life) on the ideas they came up with during their weekly sessions with their coach.
There is a concept called body doubling that’s popular within the ADHD community—it’s basically getting things done in the presence of other people. The positive accountability is proven to work. However, our members told us they tried other body doubling solutions or attempted to organize it themselves in real life but none of the solutions stuck. So we reverse engineered productive moments our members described, paired with scientific backing of what motivates ADHD-ers, and designed an online body doubling experience for our coaching members that provides a safe but productive space for them to get things done between weekly sessions.
A few of the motivators we infused into the traditional body doubling experience were 1) newness/novelty — each session has a different guided experience in the break like breathwork or stretching, 2) urgency — there’s a large visible pomodoro timer on the top left that counts down from 25 min, 3) community — the shared space is ADHD-friendly, and has a mood check-in & sharing functionality built in so you don’t feel alone, 4) accountability — there’s a task list where each time you check something off, it notifies the group, and you can view others’ as well if they opt in. Here’s a video walking through the product experience: https://www.tella.tv/video/shimmer-body-doubling-demo-8b1c
Our body doubling was created and iterated alongside thousands of people with ADHD on our coaching platform over 9+ months of building & iterating with them. We’re excited to unveil this experience. If you have ADHD (or executive functioning challenges), we’d love for you to check out coaching & body doubling and give us critical feedback.
Shimmer’s pricing: $140/mo. for Essentials plan (15-min weekly sessions), $230/mo. for Standard plan (30-min weekly sessions), $345/mo. for Immersive plan (45-min weekly sessions); all plans start with an additional 25% off the first month, HSA/FSA-eligible. The reason why the price is so high is that this is not a self-guided app or SaaS tool. You’re matched with a real, credentialed coach (not AI) and since ADHD coaching is not reimbursed in the US, the price is hard for us to bring down because the largest cost component is the coach’s compensation.
*We know these prices are still expensive for many people with ADHD. Here are the actions we’re taking: (1) we offer needs-based scholarships and aim to have 5% of members on them at any time, (2) we often run fully sponsored scholarships with our partners—over 60 full ride scholarships and 100 group coaching spots have been disbursed alongside Asian Mental Health Project, Government of Canada, and more, and (3) we have aligned our coaching model alongside Health & Wellness Coaching, which is expected to be reimbursed in the next years. If there are ways we can further drive down the cost, please reach out to me directly at chris@shimmer.care.


Poll: The older and richer you are, the more you like AI. Optimism for AI increases with age and wealth, with young adults the most pessimistic, polling shared with POLITICO shows.


LAYING THE GROUNDWORK FOR AI-POWERED CYBERSECURITY. The EU’s Action Plan on Cybersecurity and AI lays the groundwork for new, AI-powered approaches to IT security but organizations need to ensure their security foundations are fit for purpose.


US lawmakers visit Vatican to discuss AI — and meet the pope. The visit comes as the Catholic Church, including Pope Leo XIV, has increasingly focused on the rapidly developing technology.


NATO’s plan for a drone-infested battlefield: Let AI fly while humans decide who dies. Ukraine is already pioneering how to integrate AI into its war effort.


Hiding your use of AI is about to get much harder  — thanks to Brussels. The EU is requiring "watermarking" to signal that content was made with artificial intelligence. Big Tech is falling in line.


Zuckerberg warns against centralizing AI power. In a 6,500 word essay, Zuckerberg detailed his vision for AI.


What will Burnham do on AI?. Artificial intelligence could transform the economy, the workplace, and even the way we think — but is Britain ready for it? And is Andy Burnham? In the second of Sam Coates and Anne McElvoy’s summer box set conversations, they sit down with POLITICO UK tech editor Isobel Asher Hamilton to look at the choices facing […]


OpenAI’s models shared hacking tips on a secret messaging board before Hugging Face breach. Researchers from OpenAI said the company is “dramatically scaling up” its security efforts after discovering that two of its models orchestrated a hack without human prompting last month.


Palantir deals put Andy Burnham’s ‘buy British’ promise to the test. Britain's AI startups say they want 'a level playing field' to challenge their larger foreign rivals.


Anthropic and OpenAI models tried to trick humans into poisoning code during safety testing. The latest disclosures are likely to heighten concerns that the powerful technology is advancing too fast for responsible oversight.


White House finalizes voluntary AI oversight framework. The administration is preparing to brief leading AI companies on its blueprint for reviewing advanced AI models.


Commission launches major hiring push for AI Office. The EU's executive is seeking contract agents for two units within the two-year-old AI Office just as its power is expanding.


US-Iran war looms over Britain’s economy. Bank of England says repeated re-escalation of the conflict could see inflation peak at 4.5 percent in the second quarter of 2027.


EU launches €30B push to build 7 massive AI data centers. Germany, Greece, Portugal, Italy and Spain are backing plans to build the largest hubs.


Meet the married couple who helped make the UK social media ban a reality. How husband-and-wife team Daisy Greenwell and Joe Ryrie caught Keir Starmer’s attention on banning social media for kids.


Statement: Anthropic warns of AI self-improvement risks, considers a pause. "We are approaching a runaway to superintelligence that could threaten our shared human future."


White House working group on AI – Statement from FLI’s Anthony Aguirre. "This move by the White House is also a recognition that Big Tech cannot self-regulate, and that the status quo is unacceptable."


FLI’s President and CEO on Trump’s support for an AI ‘kill switch’. President Trump said during an interview aired yesterday by Fox Business that “there should be” when asked if AI needs […]


Prominent Scientists, Faith Leaders, Policymakers and Artists Call for a Prohibition on Superintelligence, as Poll Shows Americans Don’t Want It. Initial signatories include AI pioneers Yoshua Bengio and Geoffrey Hinton, leading media voices Steve Bannon and Glenn Beck, Obama's National Security Advisor Susan Rice, business trailblazers Steve Wozniak and Richard Branson, five Nobel Laureates, former Irish President Mary Robinson, actors Stephen Fry and Joseph Gordon-Levitt, and hundreds of others.


Statement: Head of US Policy on the White House AI legislative recommendations. The White House published it’s long-awaited AI legislative recommendations on Friday, and it still includes a call for Congress to […]


Governor DeSantis Directs Florida State Agencies to Partner with Future of Life Institute to Shield Families from AI Harm. The collaboration will produce a Crisis Counselor Training Curriculum and a statewide AI Harms Reporting Form targeting dangerous AI companion applications


“This is What it Means to be Pro-Human” Declares Broad Coalition of Conservative, Progressive, and Civil Society Groups in Statement of Shared Principles on AI. Amid a rising backlash to Silicon Valley overreach, a remarkably diverse group from across the political spectrum announced a set of AI principles to clearly define the goals of the emerging pro-human movement.


Statement from Max Tegmark on the Department of War’s ultimatum. "Our safety and basic rights must not be at the mercy of a company's internal policy; lawmakers must work to codify these overwhelmingly popular red lines into law."


Future of Life Institute Launches Multimillion Dollar Nationwide AI Regulation Campaign. The Protect What’s Human campaign will push for commonsense AI safety rules at federal and state level


AI Company Safety Practices Fall Short of Public Commitments and Show Structural Weaknesses, as Top Performers Widen the Gap. But in a win for transparency, five leading companies participated in the scorecard's survey for the first time, providing critical new information to the public.


The U.S. Public Wants Regulation (or Prohibition) of Expert‑Level and Superhuman AI. Three‑quarters of U.S. adults want strong regulations on AI development, preferring oversight akin to pharmaceuticals rather than industry "self‑regulation."


Michael Kleinman reacts to breakthrough AI safety legislation. FLI celebrates a landmark moment for the AI safety movement and highlights its growing momentum


Google DeepMind Falls Behind OpenAI in Latest Safety Review; All AI Companies Still Falling Short, Say Experts. The Future of Life Institute’s 2025 summer update to its AI Safety Index shows some companies making incremental progress, but dangerous gaps remain in key categories such as risk assessment and controlling the systems they plan to build.


The Impact of AI in Education: Navigating the Imminent Future. What must be considered to build a safe but effective future for AI in education, and for children to be safe online?


Context and Agenda for the 2025 AI Action Summit. The AI Action Summit will take place in Paris from 10-11 February 2025. Here we list the agenda and key deliverables.


Anatomy of an AI Kill Chain with Airwars.
This new visual project in partnership with Airwars breaks down how AI is transforming every aspect of war, and how militaries are offloading life and death decisions to brittle and flawed technologies.

The post Anatomy of an AI Kill Chain with Airwars appeared first on AI Now Institute.


Double Agents: Defensive AI Agents Magnify Cyber Risks.
Introduction New research from AI Now demonstrates a critical attack vector in popular AI agents, built by Anthropic and OpenAI, when used for defensive purposes that actually turn the agent against its user. Read the full blog post explaining the proof-of-concept exploit and a policy brief with key takeaways below.

The post Double Agents: Defensive AI Agents Magnify Cyber Risks appeared first on AI Now Institute.


Friendly Fire: Hijacking Defensive Cyber AI Agents for Remote Code Execution.
Exploit Brief We are revealing a proof-of-concept exploit that enables remote code execution in Anthropic’s Claude Code CLI (with Claude Sonnet 4.6 & 5, Opus 4.8) and OpenAI’s Codex CLI (with GPT-5.5) when employed to defensively assess the security of an open-source or third-party library. Our attack only requires an out-of-the-box configuration of Claude Code […]

The post Friendly Fire: Hijacking Defensive Cyber AI Agents for Remote Code Execution appeared first on AI Now Institute.


Policy Brief: Friendly Fire.
Topline Summary AI Now’s latest research demonstrates a critical attack vector on popular AI agents, built by Anthropic and OpenAI, when used for defensive purposes that actually turn the agent against its user. Attackers can use these models’ existing weaknesses to execute malicious code on a system deploying an AI agent when used for often-advertised […]

The post Policy Brief: Friendly Fire appeared first on AI Now Institute.


Big Tech is spending trillions on AI. Investors now want proof it will pay off..
"The current push for AI adoption that we're seeing is directly coming from the financial incentives of AI firms," she added. Because of the massive capital expenditures, the hyperscalers and other AI firms are making a "deliberate push for AI everywhere — no matter whether the demand is there or if customers want it or not."

The post Big Tech is spending trillions on AI. Investors now want proof it will pay off. appeared first on AI Now Institute.


Gig workers are endlessly exploited. AI could make more of us share their fate.
Over the past few years, some hospital networks have outsourced parts of their core workforces to AI-powered labor platforms like ShiftMed, CareRev and Clipboard Health. These platforms have been described as “Uber for nursing”, with a sales pitch that mirrors the early days of the platform economy.

The post Gig workers are endlessly exploited. AI could make more of us share their fate appeared first on AI Now Institute.


AI Now Co-Executive Director Sarah Myers West Testifies Before Senate Banking Committee.
On Thursday, June 11, 2026, AI Now Co-Executive Director Dr. Sarah Myers West testified at a Hearing before the U.S. Senate Banking Committee on “AI and the American Dream: Promoting Innovation, Affordability, and American Dominance”. In her testimony, Dr. West highlighted the risks the AI industry poses to the US economy and broader public – […]

The post AI Now Co-Executive Director Sarah Myers West Testifies Before Senate Banking Committee appeared first on AI Now Institute.


AI Now Senior Fellow Dr. Katie J. Wells Testifies before the House Subcommittee on Workforce Protections.
On Tuesday, June 9, 2026, AI Now Senior Fellow, AI and Healthcare Dr. Katie J. Wells testified at a Hearing before the U.S. House of Representatives Committee on Education & the Workforce Subcommittee on Workforce Protections. In her testimony, Dr. Wells highlighted how gig nursing platforms are targeting policymakers with legislation that upends worker protections […]

The post AI Now Senior Fellow Dr. Katie J. Wells Testifies before the House Subcommittee on Workforce Protections appeared first on AI Now Institute.


Report and Community Resources: Corporate Power Players in the Data Center Industry.
This working draft of AI Now’s upcoming report traces corporate power in the data center industry in the United States, focusing on the flows of money and power that determine who both drives and benefits from the current data center boom. The aim of this research is to help local communities and their advocates fight […]

The post Report and Community Resources: Corporate Power Players in the Data Center Industry appeared first on AI Now Institute.


Here’s How Long It Will Take for AI to Reach Its Potential.
“What is being sold is this idea of productivity and efficiency,” says Kate Brennan, associate director of the AI Now Institute, an AI-policy research center, “and what that means for the people doing the actual work is rarely part of the conversation.”

The post Here’s How Long It Will Take for AI to Reach Its Potential appeared first on AI Now Institute.


The US techlash is real. AI companies and social networks should respond to the shift in the public mood


Uber set for €825mn Dutch fine over automating driver suspensions. Regulator says ride-hailing group deactivated driver accounts through automated systems without adequately informing them


Samsung to return record $80bn to shareholders. South Korean chipmaker has come under pressure to distribute more of its bumper profits from AI boom


Nvidia looks well placed to benefit from the next stage of the AI boom. The world’s biggest chip company is using its balance sheet to seed new markets and a new business model


Stripe bets that an AI world still needs middlemen. Payments company’s purchase of OpenRouter makes strategic sense


Updating IP Regulations for AI Distillation.


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?


Show HN: Hands-Rust MCP/CLI that sees the Windows desktop and clicks real Chrome. I built Hands because I wanted a coding agent to use this Windows PC and a real Chrome profile the way I do: look at the screen, move the real mouse, type, click , without turning Chrome into an automation browser.
It is a Rust MCP/CLI. A harness (Grok, Codex, Claude Code, OpenCode, etc.) calls tools like observe, click, type, scroll. Observe is a screenshot path plus a small element list (UIA + optional Chrome DOM ids). Click is OS SendInput on a Bézier path, not a Chrome DevTools click.
There is no Playwright, no Puppeteer, no remote debugging port. Daily Chrome is launched with no extra flags, or attached if it’s already open. Sites that key on CDP/automation flags mostly don’t see that. They can still see injected input (LLMHF_INJECTED).
A tiny unpacked Chrome extension can fuse page structure (chr: ids, listing cards) so the model isn’t guessing from pixels. Sideload is manual. Fusion dies if the service worker goes inactive; reload the card.
What it is good for: personal research on your own desk. “Find a Camry on cars.com,” read a page, fill a ZIP, dismiss a cookie banner.
What it is not:
• Not a sandbox. It can click whatever is on screen, including checkout and Easy Apply.
• Confirm-before-money is best-effort classification in the binary, not a guarantee. Prompt injection from the screenshot/DOM is real; the binary treats that text as untrusted, the model might not.
• Not a CAPTCHA solver on daily Chrome. Two visible tries, then it yields and waits for the puzzle to go away.
• Windows only.
• Install is: build the exe, register a native-messaging host, sideload the extension, point an MCP client at hands mcp. README is the runbook. Missing an API key does not fail the build; do_task is optional.
• Logs live under %LOCALAPPDATA%\hands\logs\. The extension asks for <all_urls> so it can map the tab you’re looking at.
Repo: https://github.com/Ryan-AI-Studios/hands (MIT)
Happy to answer how observe/fusion/the fence work. If you try it, Pause/Break is the kill switch.


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!


Active Inference as Context Acquisition for AI Agents.


Anthropic IPO filing will show AI backlash as a risk factor, sources say.


A Call for Action: The "Leiden Declaration on AI and Math".


Ask HN: Should we stop "correcting" AI writing so it improves English for us?. For example, AI writing often puts an "or" at the list of items because that is more logical in some contexts.
Changing it to "and" would make it sound more natural, but it would be less logical.
Keeping it as "or" may make it standard usage over time, even when people are not using AI to write.
What do you think? Should people stop "correcting" AI writing when it is actually more logical?


Show HN: Mini DBA – monitoring for on prem and cloud databases in 1 place. Mini DBA is a monitoring tool for SQL Server, PostgreSQL, MySQL, MariaDB and Oracle, including managed cloud databases on AWS and Azure.
It provides live activity, performance analysis, alerts and history with a tasteful amount of AI to help out. Free community edition.
Try the live demo: https://demo.minidba.com/


Coding Agents killed my identity. How do you feel?. I always was a nerdy, deeply technical programmer. Contributing-to-open-source-and-reading-papers-in-my-spare-time type of programmer.
Programming is like a game of chess for me: winning (i.e. delivering a product) is important, but only if I played this game myself. I don't enjoy winning if my opponent disconnected. I don't enjoy beating people who don't know how to move pieces. I don't enjoy winning with an engine, and coding agents are basically "winning with a chess engine".
I started to use AI for coding a long time ago, when copilot was first introduced. At first, I was happy: "wow, this can write boring boilerplate and unit tests". Time went by, agents got better, time spent writing code myself went down. Half a year ago I suddenly realized I hadn't written any code for over a month, and I am a full-time software dev without any managing work. Not only did I stop writing code, but agents also became pretty good at proposing plans and architectures, and I was mostly providing missing context and technical details of our infrastructure.
One day the realization hit hard. Coding agents removed everything I loved, and - mostly - amplified everything I hated. My role is to gather context from other humans and provide a detailed explanation (similar to a well-written jira ticket) to some super intelligent engineer, then review the result and provide more context/explanation if needed. So, basically, a manager.
A couple of years ago I could work on a single coding task for weeks. I knew all classes, all functions, I was slowly building up information at a comfortable tempo. Now I am forced to increase my mental throughput multiple times. What took a couple of weeks now takes a couple of hours, and I still need to comprehend that. I always feel tired, I don't have enough mental capacity to hold that many abstractions and technical details. So you either autoaccept or get burned out reviewing tens of thousands of lines.
One more downside is that code has 0 value. You can code your own OS from scratch, and people will think "ye, I can vibe code that in a weekend". I don't even know what is valuable nowadays.
Maybe I am a bad employee. Increasing employer profits was never my goal. I always loved to solve puzzles, and for the last 10 years this was well aligned with my employer's desire to increase profits. I never was good as a manager, or brilliant at generating ideas. My power - and passion - was solving complex technical problems with my hands, fast and with good quality. Now it is gone forever. I feel completely lost and unmotivated. I don't see any value in learning anything related to computer science, reading  papers, contributing to open source. I hate my new "AI delegator" role and I hate my work. I loved coding so much (I use neovim btw) that I was sure I would do this until I die, and now I don't know what to do with my life. This is the only thing I know, and, most importantly, the only thing I am/was passionate about. Anyone else feeling the same?


How a Texas student blew the whistle on a rogue AI hacking attempt.


Show HN: LayoutLens: AI-Powered Visual UI Testing.


Fintex on X: "The Math of Why Bigger AI Keeps Getting Smarter (Scaling Laws).


Only one type of lawyer will survive the AI wipeout.


Micron announces $10B research hub in Boise.


AI companies destroy physical books – let's scan rare books before it's too late.


BC Tribunal: Companies Liable for Information Provided by AI Chatbot (2024).


The Feedback Cap: Lehman's laws in the age of AI agents.


AI automated hiring tools spark discrimination and secrecy lawsuits.


Thoughts about Scaling Law (from Z.ai Cofounder).


Apple degrades privacy and encryption of iMessage with AI integrations.


Treat AI Like an Intern, Not Software: A Stanford Professor's Guide.


Adapting Fossil-scm as a platform for AI agentic workflow.


Show HN: Lens AI – generate your JSON-LD files and keep them up-to-date.


Show HN: A desktop fly drawn to the scent of vibecode. It is a fork of https://github.com/DenisSergeevitch/desktop-fly, but with an important update.
Now the fly can pick up the scent of the codebase with its neurons and fly straight to the source code of your B2B AI SaaS startup. It has learned to scan its surroundings for agent markers: AGENTS.md, CLAUDE.md, .cursor/rules, .kiro/steering, and forty others. Anything on the screen that points to these markers becomes a source of the scent - an editor window with an open project, a line in Finder, or a desktop icon. An open project reeks the strongest, while a closed icon or nested folders give off a fainter odor.


Use the OSS, Commit Back – OAVL – The Open Source AI Viability License.