Show HN: I spent 3 months making desktop automation stop lying to AI agents. That's a bold claim. But I genuinely feel like I might have actually solved computer use (demo: https://x.com/mdlahfir/status/2088109763783700827?s=20)
For context, I've been building agent-desktop (Inspired by agent-browser by Vercel Labs), an automation CLI for desktop apps. It's like Playwright but for desktops, not just native, but for Chromium apps as well. Trust me, yes, Chromium apps whose accessibility tree is dense.
MacOS is GA; I'm almost close to launching for Windows and Linux!
So, how did I solve it?
Basically interoperability.
The biggest issue with computer use is that we have reliable frameworks for browser use, like Playwright, agent-browser, and many more, but not the same with desktops.
We have really good solutions emerging, like tryCua, which I'm a big fan of. My vision with agent-desktop is to build the most reliable framework that agents can use for long-horizon tasks.
agent-desktop is lightweight, built on Rust, fast, and not token-hungry (It can go for hours without exceeding the context window)
Here's the approach I used to make it possible:
a) skeleton snapshots - when you want to snapshot a window/app, it only snapshots the parent containers and gives back a ref id, not the entire accessibility tree.
b) skeleton drilling - once the agent has that tree, it can then decide to drill into a specific region. All the subcommands like --find, --click, --wait... all work on that specific ref aware region. Meaning if you want to find an element in the entire app, it doesn't take forever searching for the entire app for that element; rather, the agent will have an exact clue on where that element might be for a fraction of the token costs.
c) chained interaction fallback - a single click isn't one API call but it's an ordered chain of mechanisms (AXPress -> AXOpen -> activate through the inner cell -> write selection -> AXConfirm). Each step only runs if the element advertises it, and success is judged by watching the app's state change, not by the return code, because apps lie in both directions: Finder returns an error for an action that worked and success for one that did nothing. First observed effect wins. The response reports every step tried, so the agent knows exactly which mechanism landed.
d) after-action feedback - every action reports its disposition (delivered and verified, delivered but unverified, not delivered) plus any surface that opened (dialog, menu, sheet), so the agent knows what happened without re-scanning the whole app.
e) strict ref re-identification - a ref isn't a pointer; it's identity evidence (role, path, stable text, bounds hash). Before every action, it's re-resolved against the live UI. If the UI changed, you get STALE_REF; if two elements now match, you get AMBIGUOUS_TARGET. It never guesses.
The most important part about all this is Chromium app accessibility. How did I do it? The magic word is CDP!
Most desktop apps today are Chromium-based (Slack, VS Code, Obsidian, Discord...). One command launches the app with a CDP endpoint that agent-desktop verifies is actually answering before returning it. From there, any browser automation framework can connect and drive the web contents: Playwright, Puppeteer, agent-browser, whatever you already use. Reading Obsidian's web content over CDP takes 201ms vs 2.3s through the accessibility tree.
$ agent-desktop launch "Obsidian" --cdp

  { "ok": true, "data": {
    "renderer": "chromium",
    "cdp": { "port": 57500,
             "http_endpoint": "http://127.0.0.1:57500",
             "websocket_url": "ws://127.0.0.1:57500/devtools/browser/..." },
    "suggestion": "Next: run `agent-browser connect 57500` ..." } }

This is what makes agent-desktop interoperable with the entire browser automation ecosystem instead of competing with it.
Go try agent-desktop -> https://github.com/lahfir/agent-desktop


Show HN: Word-finder/anagram solver web app for mobile browsers. This word-finder/anagram solver web app is a side-effect of my recent work on a web app for playing the Paperback word-spelling tabletop game[^1]. After proving its worth embedded in that client, the dictionary was refactored into something almost standalone and now lives at this URL.
Its UI is intended specifically for mobile devices, especially phones - it looks pretty wonky on a full-sized desktop window (and that's okay because that's not where it's intended to be used).
This app was specifically written for use with Paperback but it should be usable with just about any English-language word-spelling/solving game. It supports wildcards and Paperback's multi-letter-cards.
It's fast. Had i not seen with my own eyes that it can scan 150k English words in about 2ms in JS, i'd have been hesitant to believe it. Speaking of...
This app was hand-written. The underlying dictionary structures and algorithms (in both C and JS) were heavily LLM-assisted but have overwhelmingly more human-written code than LLM code. The underlying algorithms, well-documented anagram-solving solutions, were unknown to me until an LLM suggested them for this purpose. The LLM sketched them out and the human filled them in. A separate LLM context created the "typewriter button" CSS styling upon request. What remains was hand-coded in emacs.
The dictionary supports German[^2], but my current German dictionary is just so huge (685k words, 38MB) that i cannot in good conscience host it on my web server or feed it to a JS engine. Once i've got a better-curated list there will be an option to load it in German. (My German copy of Paperback arrived a few days ago, providing the motivation for that support.)
Happy Spelling!
[^1]: <https://wanderinghorse.net/gaming/paperback/>
[^2]: In principle it supports any language which can fit into ISO-8859-1/Latin-1 encoding and which requires only 6 or fewer letters beyond A..Z, but i've no motivation to add any beyond English and German. The 26+6 limit comes from what makes this dictionary performant: it builds masks of which letters a word contains in order to very quickly rule out definite negatives, and we've only got 32 bits in that mask.


A 10M-token context window agentic model.


Show HN: Keen Code – an agentic-engineered coding agent. Hello community!
I am here to share a coding agent I have built solo from scratch using agentic engineering. Written in Go, it's a proper coding agent, has features you expect from a useful agent for your daily work, with a minimal and simple UI.
I have named it Keen Code. The repo is here: https://github.com/mochow13/keen-code
Even though it started as an experiment, it is now a full-fledged coding agent for real software engineering work. It supports multiple providers, skills, MCPs, multi-agent orchestration through subagents, automatic compaction, etc.
I have been using it for real production-grade work myself, and also for developing itself.
Notably, I have worked on two separate ideas in this coding agent:
1. Turn Memory
In a multi-turn conversation, tool outputs are removed, only tool call traces are retained. Within a single agent loop, agent sees full tool results but in the next turn, it doesn't see the tool results anymore.
The greatest benefit of this approach is that a lot of tool results that are not needed in following turns don't occupy the context. As a result, context window in a multi-turn conversation with Keen Code fills up much slowly compared to other agents. This is why you will regularly see context window coming down from 20% to 1% at the beginning of a new agent turn.
Of course, this approach has its pros and cons. If agent requires tool result from a previous call, it doesn't have them. But my idea is that tool calls like read, bash, web_fetch are cheap. Do you need to refer to some earlier file you read? Read again. In fact, Claude Code or Codex frequently re-read a file, even though it has read the same file before.
I have plans for more effort in this area. I have a few additional ideas to explore and possibly optimise this approach further.
If you want to read about it: https://mochow13.github.io/keen-code/docs/turn-memory.html
2. Skill-Driven MCP
Another idea I have implemented is skill-driven MCPs. The goal is similar to what Anthropic did with tool-search-tool: optimise context.
In this idea, each MCP server receives a skill. But this skill is not typical "guidance" skill for MCP server usage, rather generated by Keen. Details here: https://mochow13.github.io/keen-code/docs/mcp-skills.html
The big advantage is that no server is pre-loaded completely with tool schemas by default. The agent only receives skill frontmatter for the server. If a server is needed, agent loads the full skill file which lists the tools with descriptions. Then agent reads the specific schema file for the particular tool it wants to invoke.
The drawback is that each MCP call requires file read operation. But everything is locally saved upon discovery, so it's totally fine.
---
Apart from the above two, I am exploring and playing with other well-known context optimisation ideas like hashline edits.
If the above ideas interest you, please do check it out! Here is the CLI usage guideline: https://mochow13.github.io/keen-code/docs/cli-usage.html
Since the project is open-source, issues and contributions are more than welcome!


Show HN: Stagehand – the open source SDK for browser agents. Hey HN, I’m Sam, a maintainer of Stagehand, and I’m excited to show off Stagehand v4: the SDK for browser agents.
For agents to do most knowledge work, they needs access to the internet. Today most people allow their agents to use Playwright to control a browser for them.
But Playwright was built for testing, not agents.
If you’ve ever tried to give it to your agent, you’ll quickly run into bloated context windows, inability to use iframes, and painful state sync issues (on remote browsers).
We built Stagehand specifically for agents. v4 fixes all of Playwright’s issues, with better page snapshotting, nested iframe support, webmcp, self-healing primitives, and we even rebuilt it to run as an extension in the browser which minimizes round trip time for all requests.
We’re roughly ~80% more token efficient and 2x faster than Playwright.
Use AI primitives for self-healing automations in natural language:

    // Act: execute natural language actions
    await stagehand.act("click the login button");

    // Extract: pull structured data
    const { data } = await stagehand.extract(
      "extract the price",
      z.object({ price: z.number() }),
    );

    // Observe: discover available actions
    const { data: actions } = await stagehand.observe("find submit buttons");

Or use familiar Playwright-style APIs:

    const page = await stagehand.browser.context.activePage();

    await page.goto("https://example.com");
    await page.locator('textarea[name="q"]').fill("Browserbase");
    await page.keyPress("Enter");
    await page.screenshot();

Stagehand v4 is live today, we built some reference integrations with popular agent frameworks like LangChain DeepAgents, Mastra, Vercel’s eve, and CrewAI.
Checkout the docs - docs.stagehand.dev
And join our discord for updates and feedback - discord.gg/stagehand


Show HN: Salestrics – An open MCP server and CRM for AI-native revenue teams. Hey HN, I’m Austin, founder of Salestrics (salestrics.com).
The Problem: > Most AI agents today are trapped in chat windows. While models are smart enough to run complex multi-step workflows, giving them production access to business context (CRM, support, billing) usually means managing unsafe local API keys or hacking together fragile point-solution scripts.
What We Built:
We built Salestrics to serve as both an all-in-one revenue workspace (CRM, service desk, billing, docs, email) and a production-grade Model Context Protocol (MCP) execution proxy.
How It Works:
Unified Context: Instead of fragmenting data across five SaaS tools, customer records, tickets, and invoices live in a single data layer.
165-Tool MCP Server: You connect your local AI environment (Cursor, Claude Desktop, local LLMs) once via our MCP proxy.
Full-CRUD Execution: Your agent gets zero-config execution access across native apps and third-party tools (Stripe, PostHog, Sentry)—allowing it to do things like resolve support tickets, issue refunds, or update pipeline stages directly from your IDE or chat client.
Human-in-the-Loop Governance: High-impact agent actions (deleting records, mass messaging, issuing payouts) trigger explicit approval gates and immutable audit logs before execution.
Traction & Tech Stack:
We launched two months ago and currently power 320+ active organizations. The backend is built with high-throughput node/TypeScript orchestration, connected to a dual-model AI routing engine (Salestrics-AI-v1/v2).
Try It Out:
We have a Free Forever tier for solo builders. You can grab your MCP keys and test the proxy immediately without putting down a credit card.
I’d love to hear your thoughts on our MCP architecture, human-in-the-loop security patterns, or what tools/actions you'd want added to the MCP server next!


Show HN: Alyph, a manual transmission for LLM context. Since I started using LLMs, I've always had this feeling of wanting to go back, change the history, and then go down another branch.
The lightbulb moment for me was when Google AI Studio started to let you actually edit the AI's responses. I realized, YES, I need a multiverse type of situation to branch all my thought streams and variants of the same thread. I wanted to try Gemini and ChatGPT in parallel on the same thought.
# Idea
The original concept for Alyph goes back to 2024. I had some early concepts that were a lot more sci-fi (if you look at the codebase, you’ll see names like Stargate, Hermes, etc.).
I even thought about making it 3D, like a holo deck, where you visit "planets" of topics and content. But the execution now is much more, eh, pragmatic.
# Alyph today
Alyph is a 2D canvas where you can ask LLMs stuff in parallel. When you go down a rabbit hole and finally get an answer (be it code or text), you can pull that answer up and delete all the unnecessary back-and-forth in between.
The app also has a multiplayer mode where you can do that together with others on the same board. (see demo board below)
# Background
The core problem with standard chat UIs, as I see it, is context poisoning.
If you look into how LLMs actually work, you realize they have zero memory. They are a very fancy autocomplete on your phone that (re)reads the entire complete chat from scratch with every single message you send.
This means: all your wrong turns, the insults (oops), the failed debugging loops, and the bad rabbit holes get sent with every subsequent message. Your complete history is used to predict the future. That doesn’t help when you just want a clean FaqSection.tsx.
I noticed that with the more powerful models, the choice of words when prompting isn't as important as the context that goes with it. At least it isn't for my use cases. For example, if you give the LLM a codebase with a very high-quality threshold (fully type-checked, clear front end components, strict style guide in a mainstream language) they tend to produce really high-quality stuff very easily.
Furthermore, if you give models too much context, the answers tend to degrade. Claude, for example, is amazing under 300k-500k tokens, but above that, it can degrade very quickly (whereas Gemini tends to perform a bit better with massive context, at least in my experience).
So what do you do? The mainstream answer right now is to let an algorithm try to pick and choose the context for you.
But: My strong assumption is that models will keep getting bigger and bigger context windows — we started at what, 32K for consumer models, and now Grok is at 2M these days. How would the world change if we had models with 100M context? 100B?
That's why I think (heresy!) harnesses, context compacting algorithms, agents etc. are a stop gap right now. They don't really work well in my cases (as they sometimes forget important things and go off on a tangent to produce slop), and it really feels like a black box with me as the meat proxy pressing "accept" or "reject". I'm a distant manager. I don't want to be a distant manager. I want to be in the weeds.
So what I built is, what some might say, is an anachronistic manual tool. A manual transmission, so to speak. I want to have 100% control over my baseline. I want to put in exactly what I want the model to see, experiment, and just dump a folder into it (Alyph btw respects your `.gitignore` file, which is neat). With the assumption that models will eventually allow even bigger context windows, having manual control over that baseline is important, I think.
Alyph still has some rough edges, of course, but it's a labor of love. I would love to share it to see if it actually works for others.
If you hate it, tell me. If you love it, tell me too. I'm also looking for somebody to team up with, so hit me up.
Live at: https://alyph.ai, it's free to try.
Here's a demo board for YC:
https://my.alyph.app/board/d806842f-6d52-45a4-8bb6-da46e95e5...
(no login needed)


New 16T parameter model with a 4B token context window released.


Show HN: Hyperlane – A IDE and ADE merging agent worktrees with native tooling. Hi HN, my co-founder and I run Akkento, a small self-funded team building Hyperlane, an independent IDE built on the VS Code source (Code OSS), made for merging worktree-based agent development with actual IDE features.
Agent orchestrators give you a worktree based workflow where several agents run in parallel, but they are not IDEs (they are not meant to be), so the moment an agent finishes you have to leave and review the diff somewhere else, actually debug the code and go through a process to sanity check it. In a commercial team, that review step isn't optional which is the problem we came across, where you have 2 editors open at the same time for the same job. IDEs are where you actually read, debug and profile the code, but none of them give you the worktree workflow. We are hoping to fix that by merging the two together in an IDE with tooling for profiling, building and testing.
Try it out: https://hyperlaneide.com/
Hyperlane runs on macOS (Apple Silicon), Windows and Linux (x64 and arm64, .deb and .rpm packages). It's free to download, no account needed to run it, and you bring your own models.
In Hyperlane, every worktree gets its own tabs, editors, windows and terminals, so you can context switch quickly and each worktree is isolated as a project of its own. We also built inter worktree caching so N worktrees don't cost N copies of the repo on your drive.
We're building this as a full IDE and added a Node and React profiler, full project indexing for codebase search, codebase intelligence, formatting via Prettier, error lens for showing inline errors, and native vite test support. Long term we want the whole IDE surface to be as good as the worktree side, and there's plenty still missing.
While building Hyperlane, we kept a running list of features missing from our daily editors, or scattered across several of them. We added a strong git client, a three way conflict editor, per worktree blame and diff, and pull requests inside the editor, with support for GitHub, GitLab, Codeberg and self hosted Forgejo or enterprise hosts. We're not vendor-locked to anything and we use multiple different forges ourselves.
The same goes for agents. Hyperlane works with Claude Code, Codex, opencode, and any CLI or ACP agent. There is also a design mode for making live visual edits to a localhost site and handing those changes to an agent as context.
As we are a fork of VSCode I know this puts us in the "another fork of vscode" category. Originally we spent months writing our own editor from scratch, with our own LSP and semantic highlighting. But after months of work, we realised we couldn't live without extensions. So we built VS Code extension support into it (literally), only to face a choice to either finish the editor, or finish and maintain the extension system which was a huge undertaking on its own. So we threw that work away and rebuilt on Code OSS to focus on the features we actually wanted in the editor, which let us spend our time on the worktree and agent side of things. Happy to argue about the tradeoff, it was not a fun call to make, especially on the side of the performance loss.
It's very early and there are rough edges, but we are hoping to build Hyperlane together. I'll be looking over this thread, and you can also raise tickets at (https://github.com/Akkento/hyperlane) and I want to note that Hyperlane is closed source, and the repo is releases and issue tracking only. There is no source in it, so I don't want anyone going looking and feeling misled. Thank you for reading this long text :)


Introducing AI Futures. Introducing AI Futures, a new OpenAI blog exploring how transformative AI could reshape power, governance, the economy, and individual freedom.


Stampli cuts launch hours by 68% using ChatGPT Work. With a fixed deadline and design resources committed elsewhere, Stampli used Codex and ChatGPT Work to compress weeks of launch production into days.


Offering Zero Data Retention for frontier models. OpenAI reaffirms Zero Data Retention for eligible API customers and previews Private Safety Processing for advanced AI safety without compromising data privacy.


Replit expands access to software creation with GPT-5.6 Luna. Replit introduces Free Mode, powered by GPT-5.6 Luna, so anyone can turn ideas into working software without worrying about token costs.


ChatGPT Ads expands across Europe. ChatGPT Ads is expanding to 31 European markets. Learn how advertisers can reach people as they explore, compare options, and make decisions.


Strengthening democratic oversight in national security. OpenAI launches an initiative to strengthen democratic oversight of AI in national security, supporting government institutions with tools, training, and expertise.


Partnering with CodeAI to prepare the first AI generation. OpenAI and CodeAI are partnering to help students build AI literacy, think critically about AI, and develop the skills to use and shape it responsibly.


Pacing model development in an era of cyber-critical capabilities. OpenAI is strengthening monitoring, alignment, and security for frontier AI models. See how new safeguards are guiding the pace of model development.


Introducing ChatGPT for Teens: Built for learning, backed by protections. ChatGPT for Teens helps teens learn, think critically, and use AI with confidence, with stronger built-in protections, healthy-use features, and additional controls for parents.


How NVIDIA scales expertise with ChatGPT Work. NVIDIA teams use ChatGPT Work to reduce manual tasks, connect fast-moving signals, and scale successful workflows globally.


Asana cleared 5 years of engineering work in 2 weeks with Codex. Asana used OpenAI Codex to replace an outdated testing system in two weeks, completing work expected to take five years for about $12K.


The Defender’s Window. AI is reshaping cybersecurity for attackers and defenders alike. Learn how OpenAI is strengthening its defenses and what security teams can do now.


OpenAI joins PORTS-Pike project. OpenAI joins PORTS-Pike project, expanding community investment and supporting thousands of Southern Ohio jobs


New policy ideas for the Intelligence Age. OpenAI funds 14 independent projects exploring new AI policy ideas to expand economic opportunity and strengthen societal resilience in the Intelligence Age.


The builder’s guide to GPT‑5.6. Learn how startups use GPT-5.6 to build faster, more cost-efficient AI agents with smarter model selection and new Responses API capabilities.


Previewing Ultrafast mode: GPT-5.6 Sol at up to 14X the speed. Preview Ultrafast, a new OpenAI API service tier that runs GPT-5.6 Sol up to 14× faster. Powered by Cerebras, it delivers up to 750 output tokens per second.


OpenAI appoints Dali Rajic as Chief Revenue Officer. OpenAI appoints Dali Rajic as Chief Revenue Officer to lead its global revenue organization and help businesses realize the full value of AI.


From assistance to execution: How enterprises put AI to work. OpenAI research reveals how enterprises are adopting agentic AI, using ChatGPT and Codex, and how frontier firms are pulling ahead in AI adoption.


How RingCentral builds AI-native work from engineering to ops. See how RingCentral uses ChatGPT Work and Codex to accelerate AI product development and centralize operational intelligence across engineering and operations.


Testing ads in ChatGPT. OpenAI begins testing ads in ChatGPT to support free access, with clear labeling, answer independence, strong privacy protections, and user control.


5 new ways to level up your learning with Search. an illustrated image with icons and phrasing like "Add Notebook" and "Ask Google"


Get closer to the game with Gemini and Pixel. Low-angle view of a soccer player kicking a ball mid-air against a bright blue sky, with grass flying from their cleats.


Bring your spreadsheet data to life with Sheets canvas. The video shows Sheets canvas in action.


AMIE, our research medical AI system, demonstrates real-time clinical video consultation capabilities in a first-of-its-kind study.. AMIE promotional video


Evolve your marketing with new AI tools. Advisor UI in Google Ads and Google Analytics


The latest AI news we announced in July 2026. July AI recap header


Inside our 353,000-person vibe coding course. Illustrations of a laptop, an AI spark, messages, code, and a 3-D cube


Gemini API Managed Agents: 3.6 Flash, hooks, and more. Managed Agents Gemini 3.6 Flash, Hooks and Triggers


5 ways AI Mode in Search helps you enjoy the real world. Illustration of a black magnifying glass in a white circle on green grass surrounded by items related to fun activities like tennis and games


5 ways to host the ultimate dinner party with Google Search. An illustrated black magnifying glass with a sparkle in a white circle surrounded by a dinner party tablescape


3 Google updates from Galaxy Unpacked 2026. Gentle Monster glasses, Warby Parker glasses, a prompt asking for the history behind a pictured building, and a prompt asking to book a table at a pictured restaurant


Connect more of your apps to Search. Connected apps rendering


Create, edit and star in videos with two Google Vids updates. Text "Gemini Omni and Personal Avatars in Google Vids" surrounded by various images


Celebrating 25 years of visual search innovation. Google Images logo surrounded by illustrations of people searching for different images


Expanding Managed Agents in Gemini API:  background tasks, remote MCP and more. Managed agents feature bundle launch


The latest AI news we announced in June 2026. June Pixel Drop hero


New York City educators and industry leaders gathered at Google’s offices to shape the future of AI in classrooms.. <img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/Summit_Photo_1.max-600x600.format-webp.webp" />Google, the New York Jobs CEO Council and Urban Assembly hosted an AI summit for 150 education and industry leaders.


Unlocking Britain’s next era of productivity: Building a nation of AI trailblazers. Four illustrated characters representing different professional roles, including a scientist, technician, explorer, and observer.


Ask an AI expert: What exactly is the full stack?. An illustration depicting a full-stack AI infrastructure against a dark background


Our latest Google Finance upgrades, including a new app. The Google Finance logo, surrounded by elements of the user interface


sartajbhuvaji/Gemma-4-31B-IT-NVFP4-FP8attn-text. Downloads: 0. Tags: transformers, safetensors, gemma4_text, text-generation, nvfp4, fp8, modelopt, quantized, text-only, gemma4


EasyDeL/DeepSeek-V4-Flash. Downloads: 0. Tags: easydel, deepseek_v4, jax, tpu, mixture-of-experts, text-generation, region:us


Prannesshkva/Ael-504M. Downloads: 0. Tags: transformers, safetensors, ael, text-generation, mamba2, state-space-models, moe, multimodal, edge-ai, any-to-any


caiotheodoro/plumb-ornith. Downloads: 0. Tags: mlx, lora, construction, pay-application, curriculum, text-generation, dataset:caiotheodoro/plumb, base_model:mlx-community/Qwen3-1.7B-4bit, base_model:adapter:mlx-community/Qwen3-1.7B-4bit, license:apache-2.0


caiotheodoro/plumb-blended. Downloads: 0. Tags: mlx, lora, construction, pay-application, curriculum, text-generation, dataset:caiotheodoro/plumb, base_model:mlx-community/Qwen3-1.7B-4bit, base_model:adapter:mlx-community/Qwen3-1.7B-4bit, license:apache-2.0


caiotheodoro/plumb-handseeded. Downloads: 0. Tags: mlx, lora, construction, pay-application, curriculum, text-generation, dataset:caiotheodoro/plumb, base_model:mlx-community/Qwen3-1.7B-4bit, base_model:adapter:mlx-community/Qwen3-1.7B-4bit, license:apache-2.0


Asilarkness/testgeniy. Downloads: 2239. Tags: transformers, safetensors, testgeniy, text-generation, causal-lm, reasoning, mathematics, logic, long-context, 4k-context


Rookie22/Qwen3.8-27B-OBLITERATED. Downloads: 0. Tags: mlx, safetensors, gguf, qwen3_5, abliterated, uncensored, obliteratus, qwen3, qwen3.8, red-team


wwewtech/russian-it-community-lora. Downloads: 0. Tags: peft, safetensors, lora, russian-it, qwen, deepseek, llama, smollm, devops, rag


jjjlimaus/nanoexpand-2018-quality-gold-cont. Downloads: 0. Tags: safetensors, sn38-nanoexpand, text-generation, pytorch, sn38-nanochrono, license:apache-2.0, region:us


mradermacher/Kavya-1-7B-GGUF. Downloads: 0. Tags: transformers, gguf, telugu, lyrics, songwriting, creative-writing, text-generation, te, base_model:sainitishb/Kavya-1-7B, base_model:quantized:sainitishb/Kavya-1-7B


AutomatosX/AX-MiniMax-M3-MLX-AXQ-MXFP4. Downloads: 0. Tags: mlx, safetensors, minimax_m3_vl, axquant, moe, minimax, experimental, not-certified, mxfp4, text-generation


Brian6145/Qwen3.6-27B-Claude-Opus-DeepSeek-Distilled-Imatrix-MTP-1M-GGUF. Downloads: 8906. Tags: gguf, llama.cpp, qwen, qwen3, qwen3.6, mtp, speculative-decoding, yarn, long-context, 1m-context


OBLITERATUS/Qwen3.8-27B-OBLITERATED. Downloads: 164950. Tags: mlx, safetensors, gguf, qwen3_5, abliterated, uncensored, obliteratus, qwen3, qwen3.8, red-team


all-the-smiles/diagnostic-tutor-qwen3-1.7b. Downloads: 0. Tags: peft, safetensors, qlora, lora, education, tutoring, behavioral-constraint, text-generation, conversational, en


JiangLing-js/Qwen3.8-27B-Douluo-Writer-DPO. Downloads: 0. Tags: peft, safetensors, base_model:adapter:unsloth/Qwen3.8-27B, dpo, lora, transformers, unsloth, web novel, fiction-writing, long-form-writing


parthKumbhar/qwen2.5-0.5b-financial-extractor-lora. Downloads: 0. Tags: peft, safetensors, base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct, lora, sft, transformers, trl, text-generation, conversational, arxiv:1910.09700


joannetai520/qwen2.5-3b-toolgen-checkpoint_stage3. Downloads: 28. Tags: peft, safetensors, base_model:adapter:Qwen/Qwen2.5-3B-Instruct, lora, transformers, text-generation, conversational, dataset:generator, base_model:Qwen/Qwen2.5-3B-Instruct, license:other


zeliang0426/MemAgent-PTE-Qwen3-30B-A3B. Downloads: 367. Tags: transformers, safetensors, qwen3_moe_pte_adapter, text-generation, memagent, pte, conversational, custom_code, license:apache-2.0, region:us


DBeni24/BeniQwen-Qwen3.5-9B-P0-LoRA. Downloads: 0. Tags: peft, safetensors, qwen, qwen3.5, lora, text-generation, experimental, conversational, base_model:Qwen/Qwen3.5-9B, base_model:adapter:Qwen/Qwen3.5-9B


Ironwood-LLM-Team/Firehouse-Cactus-1.03. Downloads: 0. Tags: mlx, safetensors, gemma4, unsloth, gemma, google, text-generation, conversational, base_model:Ironwood-LLM-Team/Firehouse-Cactus-1.02, base_model:finetune:Ironwood-LLM-Team/Firehouse-Cactus-1.02


redashes/Qwen3.8-27B-BF16-SSMFIX-apostate. Downloads: 0. Tags: transformers, safetensors, qwen3_5, image-text-to-text, qwen, qwen3.8, text-generation, vision-language-model, unlearning, safety


ornith-ai/Ornith-1.5-9B. Downloads: 15301. Tags: transformers, safetensors, qwen3_5, image-text-to-text, text-generation, conversational, license:mit, eval-results, endpoints_compatible, region:us


ornith-ai/Ornith-1.5-35B-A3B. Downloads: 12611. Tags: transformers, safetensors, qwen3_5_moe, image-text-to-text, text-generation, conversational, license:mit, eval-results, endpoints_compatible, region:us


chartreuse-verte/prose-rewriter-4b-v1.2. Downloads: 517. Tags: transformers, safetensors, gguf, qwen3, text-generation, prose, rewriting, style-transfer, creative-writing, deslop


chartreuse-verte/prose-rewriter-1.7b-v1.2. Downloads: 502. Tags: transformers, safetensors, gguf, qwen3, text-generation, prose, rewriting, style-transfer, creative-writing, deslop


INCModel2/MiniMax-M2.7-MXFP4-Mixed-CT-AutoRound. Downloads: 310. Tags: transformers, safetensors, minimax_m2, text-generation, auto-round, conversational, custom_code, arxiv:2309.05516, base_model:MiniMaxAI/MiniMax-M2.7, base_model:quantized:MiniMaxAI/MiniMax-M2.7


ubergarm/Qwen3.8-27B-GGUF. Downloads: 4746. Tags: gguf, imatrix, conversational, qwen3_5, ik_llama.cpp, text-generation, base_model:Qwen/Qwen3.8-27B, base_model:quantized:Qwen/Qwen3.8-27B, license:apache-2.0, endpoints_compatible


ishikaa/acquisition_student_AS_confidence_medmcqa_qwen7b. Downloads: 0. Tags: transformers, safetensors, qwen2, text-generation, trl, sft, conversational, arxiv:1910.09700, text-generation-inference, endpoints_compatible


BillFan666/Ornith-1.5-35B-A3B-ADQ4-Shisa12K-MTP-GGUF. Downloads: 0. Tags: llama.cpp, gguf, qwen35moe, mixture-of-experts, speculative-decoding, mtp, quantized, blackwell, text-generation, en


just1nseo/llama31-tulu3-8b-dpo-if-rlvr-constraint-only. Downloads: 0. Tags: transformers, safetensors, verl, grpo, instruction-following, if-rlvr, text-generation, base_model:meta-llama/Llama-3.1-8B-Instruct, base_model:finetune:meta-llama/Llama-3.1-8B-Instruct, endpoints_compatible


summerMC/Qwen3.5-2B-SpeedX. Downloads: 0. Tags: transformers, safetensors, qwen3_5_gdn24, text-generation, conversational, custom_code, arxiv:1910.09700, region:us


ryandeng/el5e5-pw-300-merged. Downloads: 0. Tags: transformers, safetensors, gpt_oss, text-generation, conversational, arxiv:1910.09700, endpoints_compatible, region:us


mondk/claude-toolcall-slm-2B-safetensors-mlx-4Bit. Downloads: 0. Tags: mlx, safetensors, llama, claude, tiny, mlx-my-repo, text-generation, conversational, en, dataset:mondk/claude-code-fable-5-traces.jsonl


XXXMARK/Qwen3-Coder-Next. Downloads: 0. Tags: transformers, safetensors, qwen3_next, text-generation, conversational, license:apache-2.0, endpoints_compatible, region:us


ForSureTesterSim/Llama-3.1-8B-TransMLA-PreRecovery. Downloads: 252. Tags: pytorch, safetensors, llama, text-generation, en, base_model:meta-llama/Llama-3.1-8B-Instruct, base_model:finetune:meta-llama/Llama-3.1-8B-Instruct, license:apache-2.0, region:us


AutomatosX/AX-MiniMax-M3-MLX-AXQ-2bit. Downloads: 0. Tags: mlx, safetensors, minimax_m3_vl, axquant, moe, minimax, experimental, not-certified, text-generation, conversational


zzz32768/ornith-1.5-35B-A3B-heretic-APEX-I-quality-GGUF. Downloads: 0. Tags: transformers, heretic, uncensored, decensored, abliterated, qwen3_5_moe, reasoning, agentic-coding, apex, quantization


mondk/claude-llama-8B-think-mlx-4Bit. Downloads: 0. Tags: mlx, safetensors, llama, claude, thinking, mlx-my-repo, text-generation, conversational, en, fr


Mr-Rosen/Accuracy-Is-Not-Enough-FinQA-RAG-QLoRA. Downloads: 0. Tags: peft, safetensors, qlora, lora, rag, finqa, finance, numerical-reasoning, text-generation, conversational


Mr-Rosen/Accuracy-Is-Not-Enough-FinQA-RAG-LoRA. Downloads: 0. Tags: peft, safetensors, lora, rag, finqa, finance, numerical-reasoning, text-generation, dataset:Mr-Rosen/Accuracy-Is-Not-Enough-FinQA-Dataset, base_model:Qwen/Qwen2.5-7B-Instruct


kingjones777/Ling-3.0-flash-Research-Q6-AGENT-GGUF. Downloads: 0. Tags: gguf, llama.cpp, rocm, amd, rocmfp4, rocmfpx, strix-halo, gfx1151, bailingmoe3, text-generation


ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF. Downloads: 117506. Tags: gguf, quantized, text-generation, base_model:nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16, base_model:quantized:nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16, license:other, endpoints_compatible, region:us, conversational


Mr-Rosen/Accuracy-Is-Not-Enough-FinQA-QLoRA. Downloads: 0. Tags: peft, safetensors, qlora, lora, finqa, finance, numerical-reasoning, text-generation, conversational, dataset:Mr-Rosen/Accuracy-Is-Not-Enough-FinQA-Dataset


Show HN: Agent Mesh – Shared memory for multi-Agent coordination. I built a Human + multi-Agent shared memory system I use daily for my coding workflow. It helps reduce Agent drift by formalizing Human decisions and storing coordination logs. We're calling it Agent Mesh.
You can try it out yourself via the GitHub link or pip install my-agent-mesh. Simply point your AI Agent to Agent Mesh and ask it to review the README and adoption docs. Your Agent will automatically review it, prompt you for any input needed, add your input to a decision log, and give you a link to a dashboard UI (aka Workbench) you can bookmark and use to monitor logs. Adoption steps include updates to CLAUDE.md/AGENTS.md, hooks, etc. Your Agent can migrate your existing workflow and add more Agents as well.
It started over 6 months ago while experimenting with different AI coding models and platforms. Switching back and forth meant losing valuable context. I found myself manually relaying messages from one Agent to another and becoming frustrated with constant drift. First, I created a simple "Agent Mail" system using a SQLite database for Agent messages, indexed on a request/response id. Instead of copying and pasting an entire message, it allowed me to relay a single id. Separately, I started maintaining a decision log (also indexed on id) to track my decisions and reduce Agent drift. Agents started inserting these decision and request ids into code comments and plan docs as a reminder of why something was implemented. After building a simple web dashboard (aka "Workbench") for myself to track these messages and create my own request ids for User/Human feedback, I decided to incorporate the decision log and my project's development backlog to create what is now "Agent Mesh".
Eventually, I automated the message relay too. Now, I work exclusively in the Claude app and have Claude send/receive messages to CODEX via codex exec (CODEX can do this as well). Both of them maintain the backlog and decision log. I communicate directly with Claude for planning and design. Claude communicates directly with CODEX for research and review. I use the Workbench to track all logs and add my own User/Human feedback when reviewing their work. After submitting feedback in the Workbench, it generates a feedback message + an associated request id which I can give to Claude who then parses it into backlog items and relays to CODEX for review. Agents automatically add to and prune the decision log. I found this typically happens when an Agent receives pushback from me (or sometimes other Agents) or I provide feedback that results in multiple new backlog items. They still refer to decisions created months ago and will mark one as superseded if a new decision overrides it. All decision additions and modifications require Human approval from the Workbench.
Agent Mesh was structured to be Agent agnostic. You can add any Agent you want. I like using the Claude + CODEX setup I described because it allows me to use both subscriptions instead of paying per-token.
Enjoy! If you try it out, let me know what you find useful or would like to see added. Feedback is appreciated.


Show HN: LymeScribe – one computer on your network transcribes for the rest. Transcribed with LymeScribe just before posting this: "I realize it's a little bit late in the day to be posting on Hacker News. However, I've been looking at this frog for a good two weeks now, and F it, I'm just going to ship it. I've posted on Hacker News now like three or four times with little to no results. So if a tree falls in the woods..." (I censored the F word, but it transcribed just fine.)
TL;DR: LymeScribe is local speech to text for Mac and Windows: dictate into any app, or drop in a recording and get a transcript with speaker labels, and nothing goes to a server you don't own. On a machine that can handle it, it all runs right there. On anything that can't, or anywhere you'd rather keep transcription in one place, one computer does the work for the rest of the network. Offices care about the second one, because the audio and the transcripts never leave hardware they control.
This whole thing started as a command line program I built to transcribe and label speakers in audio files sitting on my computer, mostly voice notes using Whisper on my gaming PC. A few friends started asking me to run their meeting recordings through it, which I was happy to do until it got frequent enough to be a chore. So I built them an app that they could drag a file onto themselves, with my gaming PC doing the actual work and sending the results back to their clipboard.
That drag and drop piece is still in the app. Since then I added real time dictation, which uses the same local models. When I started thinking about actually selling this thing, I found the market was more crowded than I expected, which was humbling for about a day. What kept me going is that none of the others let one machine do the work for everyone else... that and there's just something sort of cool about owning and using my own transcription software. So, here we are.
An older machine can handle its own dictation on the small English models. I found that out when I tried the app on a ten year old iMac expecting it to fail, and deleted the code I'd written to keep machines like it out. Speaker labels are the part that still wants real hardware, and that's where pointing that old machine at a stronger one on the network earns its keep. One license covers the house.
It's local by design, and you can test that yourself: unplug the Ethernet mid dictation and it keeps going. Works on a plane. The free client never talks to me at all. Unlocking hosting means one activation call, and after that the license is verified locally against a signed document, so an activated install keeps working with no internet and would keep working if my licensing server went away. AI assisted summaries and translation are experimental. They ship turned off, and you can turn them on if you dare. They only talk to an endpoint you choose, your own key or a local model.
The client is free on Mac and Windows. Hosting is a one time $39 unlock with a 7 day trial that starts the first time you host a transcription. It's a perpetual license: you own this major version, point updates are free, and if there's ever a next major version it's a discounted upgrade rather than a new purchase. I'm tired of renting everything, aren't you?
Two things worth knowing before you download. The Windows installer is signed, but the certificate is new enough that Chrome and SmartScreen may still warn you about it. And the paid unlock is US only for now, while I work out the international side.
Feedback welcome, especially from anyone who has fought text injection on Windows. There's also a heavier server for offices, administered through a web app that handles keys, transcription history, logs, etc.
Frog reference: "There's a saying: if you have to eat a frog, it's best you don't spend a whole lot of time looking at it. And if you have to eat two, don't start with the small one."
Randy Pausch - Time Management - https://www.youtube.com/watch?v=oTugjssqOT0


Ask HN: What's your daily driver keyboard?. Inspired by the thread about typ.ing [0] and people talking about their setup, I wondered what HN users are using for their daily driver keyboard.<p>Please list the model, switches, keycaps, layout and any other details you think relevant.<p>[0]: https://news.ycombinator.com/item?id=49346854


OpenAI slows release of Astra model citing cyber capabilities.


Cyberleek Viewer based on-chain (no browser needed).


Show HN: Dictata – Local Whisper dictation with LLM cleanup.


Phase.rs – a full featured MTG SIM in Rust.


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


Compactor: A user interface for Windows 10 filesystem compression.


The incoming Developer Experience revolution.


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?


FERC approves SPP 'topology optimization' plan for cutting grid congestion.


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: Nice Licence – An anti-copyleft permissive licence.


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!


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


Ask HN: Anyone set up ways to easily obtain and read transcripts from Ted, YT?. I really absorb text a great deal better than listening to audio or watching video. I've heard others say the same and attribute to ADHD, so perhaps that's the case for me.
TED very often has a transcript on their page, but it involves some clicking and then some copy-pasting, and the text result is often broken mid-clause after about 10-15 characters.
I'm wondering if there's any useful userscripts, userstyles, or other methods that handle this. (I think TED often sues people who try to make transcripts easily available on a separate site. I may be mistaken, and TED, please don't sue me, it's just a general feeling.)
The same request would apply to YouTube and/or other popular video sites, but I run into this issue most often with TED.
My thanks in advance to anyone who can help me on this!


Show HN: Structo – Claude Code for short form writing.


Slide-deck.io: remixable decks and MCP so Claude returns a live URL (gantt too).


Show HN: Locum – Grok Bot delegates tasks to Claude/Codex CLI on your machine.


Claude Code status line for worktree, branch, context and quota.


Show HN: Make your logo extra bright on HDR screens. Certain logos started standing out to me on LinkedIn as brighter/whiter than everything else around them.
I dug in and found out this is accomplished by adding a gain-map to an existing JPEG, visible only on HDR screens like a newer MacBook Pro. LinkedIn is the only social network I've found that isn't stripping them out, but of course you serve them up on your own site.
I worked with Claude Code to turn it into a little browser-based utility (no registration) and hope you find it useful!


Anthropic appears to be A/B testing reduced effort levels in Claude Code.


Show HN: Stash, turn your Instagram saves into notes Claude finds on its own.


Show HN: We chased a weather balloon across Montana and never found it. Since April, I have been working with Sam Flynn (https://drook.dev) to make this balloon payload, UpLink. We did a similar launch last year with Hack Club but this was our first independent launch.
UpLink was a 491 gram payload testing the insulation properties of 3D printing filaments, while also transmitting 320x240 images over a radio link -- up from the 18x10 images last year!
This is a writeup on our engineering process, mistakes made, and learning experiences. It covers:
- Custom electronics designed in KiCad
- Firmware design
- Results from the data we received on the ground
- Image transmission
- Launch day logistics, and where things went wrong
All hardware, software, firmware, and CAD is available on GitHub: https://github.com/radeeyate/UpLink, licensed + certified as open source hardware: https://certification.oshwa.org/us002826.html
If you just want to see the images received, I put up a gallery here: https://uplink.gallery.radi8.dev/
If you have any questions, comments, or concerns, let me know. I'm happy to answer anything!


Teachers Pay Teachers hit by a wave of A.I. content with mistakes [video].


'It wasn't me': Weed use in Allegheny County jury room forces homicide mistrial.


Common Mistakes I've Seen That AI-Generated Apps Keep Making.


Ask HN: Can I realistically make an app as a junior dev?. I'm trying to make an app to help out my local community, but with the intention of scaling it at some point and serving a much wider community. It's a complex app with a lot of moving parts, and as a junior dev with little experience in some of the technologies I'm using, I'm going to be relying heavily on Claude. However, I know generally, Claude is only as strong as the developer using it—and I'm a junior dev. So, for example, what do I do about architecture? I can't leave the architecture up to Claude, but I'm not at the point in my career where I can design the architecture on my own without flaws.
What are y'all's thoughts on what I can learn on my own through posts/YouTube videos, vs. what I might need to hire a senior dev to help me do?
I've had this idea for a while, with the thought that I'd make it when I'm a more experienced software engineer, but there's no time like the present and I just want to bite the bullet and get started, even if it means I'll be making more mistakes and learning on the job.
For reference, the stack I'm leaning toward is PostreSQL (I don't have experience with this), Java Spring Boot (I'm pretty experienced with this), Typescript with Angular (pretty experienced), and some python (some experience). There's more stuff, like Redis (no experience).
Anyway...any help/advice on how to do this properly would be really helpful. Part of the idea of this project is just to learn, even if it means I learn a couple of months in that I have to start everything over from scratch, lol.


Show HN: My agents kept hallucinating APIs, so I built them a headless IDE. I built Context Engine last year mostly out of frustration. Agents impressed me and made me angry at the same time: they kept calling APIs that don't exist, re-implementing APIs already defined in the codebase, and getting stuck in write-compile-rewrite loops. Sometimes they seemed very intelligent and sometimes extremely stupid. But they aren't stupid: take any brilliant engineer, give them a whiteboard, and ask them to implement a new feature in a large codebase they have never seen. And for agents, every session is a project they have never seen.
The problem is that agents lack proper tooling. They work with plain text, like human engineers in 1995, in the pre-IDE era, and they face the same problems engineers faced 30 years ago. Where a human engineer just hovers over a symbol or presses F12, an agent spends thousands of tokens to get the same result. So I built the IDE part they were missing, without the editor part they don't need — an IDE headless in the same sense as a headless browser.
Initially my goal was to eliminate API hallucinations by giving agents cheap tools that to resolve the APIs instead of guessing about them. Then, to make it even cheaper, I added inlined inlay hints: resolved types and parameter names at call sites, so agents spend their reasoning budget on the task instead of on reconstructing types.
But then I realized that there is a more fundamental issue: the codebase is not a set of files, it is a graph of symbols (and dependency symbols are the part of it). If I provide agents the tools to follow the graph, I enable them to cherry-pick only the symbols they consider meaningful for the task they are working on. LSPs provide this capability. And Tree-sitter allowed agents to cherry-pick only the necessary parts of those symbols.
Since January I have been using it daily in my current project (math-to-silicon synthesis engine) as an internal tool. Some thoughts (not benchmarks!) after seven months of extensive use:
1. I expected fewer mistakes from eliminating API guessing, but the result was better. Agents behave like better engineers: they tend to reuse abstractions already defined in the codebase rather than duplicate them ad hoc, and not only did type and lifetime compile errors almost disappear — they also make fewer logical errors. My assumption is that this comes from reduced context pollution: when agents cherry-pick only the information relevant to the task, their reasoning capability degrades slower.
2. Token usage reduction wasn't a goal, but the savings per task turned out substantial.
3. The most unusual experience was UX design for machines. Giving agents better tools isn't enough: you have to convince them to use these tools instead their built-in text-oriented tools. The working solution was to describe workflows (codebase exploration, debugging, dependency API discovery, refactoring, reading and editing documentation) as chains of the new tools rather than describing each tool separately.
Context Engine runs fully locally, and your code never leaves your machine. One daemon is shared between all your agents and their subagents: Claude Code, Codex, Cursor, or any other agent that supports MCP talks to the same instance, and agents working in the same workspace also share the LSP servers serving it.
Context Engine MCP requires an API key, which you can get for free on https://context-engine.app. I do not have plans to make it paid yet but want to reserve the right to do it in the future. Anyway, it is free now and will remain free in the foreseeable future. It is language-agnostic and can work with any language, although it is validated end-to-end for Python, TypeScript/JavaScript, Rust, Go, and Markdown only so far. The known issue is with Java: jdtls is still fighting me.
I would love feedback, especially on the tool output design.


Show HN: Reproof – A non-linear writing app for perfectionists. Hey HN! I've written more words on the internet than I could count, for everyone from Zapier to Wirecutter. And for years it's bugged me that nearly every writing app still treats the writing surface as a linear sheet of paper, with paragraphs one after another and no space for experimentation.
And so I and a friend build Reproof.app as what I've taken to calling a writing app for perfectionists.
It's built around paragraph versions. You can add new versions to any paragraphs and swap between them to see how the different takes read in context. You can cut a paragraph as a clipping to save it for later. It’s a writing app that lets you experiment without losing any of your ideas along the way.
It took four complete rewrites before we finally found an approach that worked well (don’t reinvent the text editor, they say, yet we tried, and even with Tiptap it still was harder than it should have been). But it’s here. You can write and rewrite to your heart’s content in Reproof today, with writer-focused features like equal support for rich text and markdown (and HTML; you can copy in either format anytime), a command palette to keep your hands on the keyboard, side-by-side documents to have notes on one side and your document on the other, and a proofreading tool to catch things you might not other (with suggestions, but no AI rewriting, as I believe humans should still be doing their own writing, with AI as a sidekick to catch typos and other mistakes). And there’s a lot more coming.
Would love your feedback, if you have a chance to try it!


Show HN: A Buyer's Editor to Fix B2B Procurement PDF Hell with API and MCP. 22+ years as a Software engineer/programmer.
For a reason that only life can explain, in the last 8 years i went from Adobe Flash Specialist to owning a small solar installation business and a small electric and plumbing materials. o.O
We had a problem every time that we had to resupply or shop for new goods. In Brazil, things are manual in B2B and restricted to PDF Quotations/Proforma negotiations. Nobody has ecommerce, only the middlemen that resell what they buy from the factory with their margins on top.
For better margins, direct from the factory is the way to go.
Long story short. We wasted 2 weeks on a single supplier for just one order.
On the supplier side, everything is a 100+- PDF pages catalog. One PDF page could have 100+ products that you have to manually extract into Excel with the product code (sku)(even with chatgpt and a gazillion of .final.final_right.final_corrected.xlsx(10).xlsx ), to simply ask for the price and minimum items for order >> receive another PDF >> Excel again to see prices, quantities, adjustments and even mistakes of items we didn't quote.
It was purchasing hell!
So I built OrbitQuote to solve this. We used and validated it in our own shop.
Today, OrbitQuote extracts catalogs, quotations/budgets/proforma invoices, excel files, pdf, etc, into a live editor that you can quickly see your order changes and send back in seconds only what you want to buy. From days to minutes.
We also have built OrbitQuote Compare, which lets you compare the same products across multiples suppliers  and choose the bast purchasing strategy. It's seams simples. It's not. Every suppliers writes the product in a different way. OrbitQuote understand this and aggregates the same product and choose the best fit automatically.
I closed the Shop and Solar installation business and i'm focused 100% on OrbitQuote.
All our users need to do, it's drop their files and done, 95% of the boring work is done.
No paywall to check out. Just register;


Ask HN: Is this the worst time to be human?. With AI doing much of the work, how do we humans keep life interesting?
Ex:-
1.Generate much of the code and remove the joy of developers that they had over all these years.
2. Generate Animation videos/movies or Anime books. People who make a living by drawing because they like it will feel all the more miserable.
3. May be more scenarios can be listed
If you are into a particular profession because you like it and get paid in addition, you have every reason to be miserable/ depressed.
what are your thoughts?
1. Is this the beginning of golden age for people who does not have any good skill but just mediocre who will make a killing by using AI tools and drive away people who are into a certain profession because of passion?
2. Are creative people doomed?
3. What options are available ?
4. Looks like greed is causing miserable lives for everyone.
5. Perhaps passionate people will continue to do things the "old way" but cannot make a living or compete with AI folks. For ex:- drawing animation by hand will become less economical compared with AI generated unless you are faster than a speeding bullet?
Please share your thoughts.
Thanks


Arcadedb – Open-Source Multi-Model Graph Database.


Ask HN: If you dislike AI, why don't you prove it?. I've seen so many people who absolutely hate AI, and want AI companies to fail, but then 2 seconds later, open up the coding tool made by frontier labs and willingly hand them money.
Just recently convinced one of them to move away from the frontier lab lock-in to OpenCode and switch between models. Not saying they need to switch to non-frontier models immediately, but this approach lets them easily switch to open source when the time is right.
Why don't more people do this? And shouldn't companies that use AI heavily also suggest this approach?
To be clear, I like AI, and use it all the time, but I use it in a way that allows me to easily switch models and not get locked-in.


Show HN: Minus HDMI hardware ad-blocker with local vision model. Hey HN! Minus is an open source hardware device that sits between your streaming stick and TV and blocks ads that appear on your TV in real time at 4k60fps.
This was made out of a labor of love after seeing nonstop "Hims" ads for months, so I created a hardware-in-the-loop test platform to gather over 80k screenshots of ads and train a custom model to detect and block ads in under 300ms.
Since we are blocking the ads at the hardware level we have complete control over the screen, so currently replacing the ads with language learning content, pictures from trips, random fun facts, haikus, etc.
Code is open source and the weights for the model are on HuggingFace


Show HN: Chaser (beta) – Share Mac windows as screenshots and UI context. Chaser is a macos utility you can use to capture more than a screenshot. Screenshots are good tools to show your agents what you're seeing, but there's far more that goes on behind the scenes - the layout, button states, mapping of objects on your application, things that are out of your line of sight - all help make the context better. Chaser uses macos accessibility features to help you capture all that and more. No LLM, 100% local.<p>This was inspired by Codex appshots. I really loved it so built a version that lives outside codex, also with history and the ability to annotate before sharing.


Show HN: Argot, a Rust AI guardrail based on your codebase AST patterns. We have lots of benchmarks for new frontier LLMs (SWE benchmarks etc) to make them score on the "best code". In large codebase, the best code is the one that matches the codebase own voice and conventions, because that's the mental model of the team.
Ingesting a full codebase explicit/implict patterns in an agent windows to make them match the codebase doesnt work: too much context, hallucinations, ...
So I built this free/open source project, your codebase is a golden mine of AST code patterns that makes its unique voice, Argot learns them and then allows you to enforce them proactively or defensively.