Docs.dev Your Own Hosted Docs Platform in Minutes
Hey we are launching Docs.dev, a full feature docs product that lets you host your developer docs on Cloudflare in a couple minutes. Why this exists: We don’t think every early stage startup should need to pay $300-$500 a month for beautiful docs, in fact every startup should just have beautiful human and agent ready docs ready to go. Additionally the workflow for docs is a lot different now in that Claude Code or Codex will generate 60-80% of the code, so why not have them update your docs as well. After they generate the docs, the missing piece is there will be some sentences, sections, and codeblocks maybe you want to eyeball or edit. The Docs.dev docs allows you to do it right in place in that your docs powered by Docs.dev, is the place that both your readers can learn and read about your tool but also the direct place you can edit your docs. If you want to check out how it works see the demo at https://docs.dev. Some features: - Ask AI using your own Cloudflare AI - Docs are editable and publishable in place exactly how users see the docs - AI generation tools built into the editor(again using your own Cloudflare) - Full markdown support for agents, every docs page can be served as MD - Docs can be served as MCP - Pretext.js editor allows images to be inline with the text Built upon other cool technology: - Fumadocs - Pretext - Cloudflare primitives - Other cool libraries This is a side project from us at Keyboard.dev, so right now we want to support making this product better but we are looking at this more as a gift to the community. Essentially before our pivot we had the domain for docs.dev so wanted to build something cool in the docs space.
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Cogram Studio
Hi HN, Rick and Alex here, co-founders of Cogram. We’ve been making project-management software for architects and engineers since 2023, and are now experimenting with a second product. Cogram Studio (studio.cogram.com) is a CAD and BIM workspace for AI agents to create three-dimensional models and dimensioned drawings. Studio runs FreeCAD (https://github.com/FreeCAD/FreeCAD) 1.1 headlessly, using the OpenCASCADE (https://github.com/Open-Cascade-SAS/OCCT) geometry kernel. It imports and exports STEP, IFC, STL, DXF and FCStd files. Studio exposes an MCP server, so you can operate it from Claude Code, Codex, ChatGPT, or other agents. Tools cover scripting against FreeCAD, inspecting the model visually, CRUD operations on views, tables, and sheets, importing terrain, etc. Studio also provides a built-in agent based on Pi (https://github.com/earendil-works/pi) and a basic UI for 3D model inspection, measurement, view, table, and sheet creation. There are two ways to try it: 1. Bring your own agent. Point Codex or Claude Code at studio.cogram.com/skill.md and ask it to follow instructions there. This starts a one-hour Studio session and opens the modelling environment in your browser, connected to your agent. You need to confirm sign up only if you want to save the session. 2. Use Studio’s built-in agent. Submit a prompt at studio.cogram.com and sign up. You receive 50 free credits. Here are two models created by agents: - A simple bookshelf (https://studio.cogram.com/view/shr_1agb21nd4) with two sheets - The Colosseum (https://studio.cogram.com/view/shr_3tm32c64y) [large, can take minutes to load], with two sheets showing dimensioned and annotated views. For now, Studio works best when you use it iteratively. If you ask an agent for a finished, complex model and “let it rip”, it may produce something that looks convincing but doesn't hold up to scrutiny. Agentic modelling feels roughly like where agentic programming was in 2024: useful for speeding up repetitive or bounded tasks but not yet reliable enough to complete long, complex jobs. Recent models have improved at CAD. We expect progress to accelerate as models become even better at reasoning across the different representations used in architecture and engineering: geometry, drawings, images, materials, physical constraints, specifications, regulations, code, etc. Early users and our own experience suggest that agents are already useful design collaborators. They can help with repetitive CAD work, fabrication options, part sourcing and cost estimates. We expect that as models improve, agents will be able to explore many different designs, run simulations, and check constraints across an entire project. CAD will become a shared workspace for humans and agents. Because design decisions made in CAD shape much of what is eventually built, even modest improvements could have wide effects. We’d love to hear your feedback and ideas if you give Studio a try. We’re also hiring across product, engineering and commercial roles. If you’re interested in working on interesting problems involving AI and the physical world, please take a look at our open positions at https://cogram.com/about#careers.
Cushion
This project mostly came from knowing that Deno KV is backed by FoundationDB and that there were (are?) plans to use Foundation for CouchDB 4.0 too. I like working on Deno. I like working in CouchDB. I like Deno KV but don’t enjoy the ergonomics of it. So Cushion was mostly made for my comfort. PouchDB with Deno KV as the storage adapter.
I built a tool showing how AI providers (should) throttle their models
OP here: this project was born out of the frustration/paranoia that AI providers are throttling their models when their server load is too high. So, I set out to model and study the problem mathematically to understand what was happening, what I found was quite surprising. The idea seems natural: as the data center demand increases momentarily through the day, throttling their models (either using quantized versions, reducing the context window or lowering the tier of the model to a smaller one) seems appealing as the replacement model in principle uses less electricity. The problem is that this can cause the opposite effect: as users are trying to solve a question, if the degraded AI model gives a bad answer, the user is likely to keep re-asking. On the AI provider side this looks paradoxical: throttling to a lower model creates in fact more demand for their data center. This problem is even worse for agentic workflows, as these are more likely to create a re-ask storm, and maybe explains the outages and anecdotal experiences of users that feel the models are degraded. The model: I used mainly queueing theory arguments solving the optimal scheduling serving for an AI fleet with heterogeneous users solving a finite horizon Dynamic Programming optimization problem. Insights: The industry standard practice of throttling once the number of users in system exceeds a given threshold is in fact what’s causing the problem, the optimal rule implies separating users that won’t feel degradation as much with users that are very sensitive to it (agents and power users vs users doing simple tasks). Limitations: The visualization and paper examples are a toy example to illustrate the problem, only the providers have enough data to properly calibrate these instances. In the paper there are some interesting calibrated instances. Technical Details: The visualization is around 100 lines of flask plus js frontend (LLM assisted with ground truth based on the original numerical example of the paper). Paper with proofs/theory: https://arxiv.org/abs/2608.23986
OzBrain, a shared brain for knowledge between agents and your team
I think agent-first chat interfaces will be a primary software modality and busy dashboard/UI will go away. I’m not sure who exactly wins it, but I want my knowledge to grow/go with me. A lot of the “knowledge” ie research, analysis, reasoning will be done by agents as the primary user. Our current notes tools & tasks management systems were built for humans… I don’t care what the 17th thing on my bug backlog is. I want to conduct agents that can execute for me and do great work. What I built OzBrain to do: + Create a central place for agent reasoned knowledge to live + Be agnostic about what apps/agents connect to it + Capture everything and track it so I can audit it + Enable teams, collaborators or partners to share brains + Handle conflicts so many agents in the same article doesn’t blow up + Refactor knowledge into more token friendly chunks and map the index well + Close the knowledge loop so new thinking supersedes old thinking across the corpus. Don’t erase, depreciate and link + Keep user data safe and secure ++ Be easy enough to use that you don’t have to have any technical knowledge Some among us will always build their own custom solutions, but there are millions of tech professionals and small business owners that will use agents heavily and need a solution. So I’m trying to build that. Isn’t this like gBrain? Yes, similar. I think it’s like AWS vs Vercel. AWS is very powerful, configurable, and useful if you’re technical and want to invest the time into really fine tuning your system… but if you just want your web deploy/hosting to just work and be easy to deal with you use Vercel. // WHY I MADE IT I’ve been enjoying getting back to my technical roots, as I lost my coding skills more than a decade ago, but with AI I can focus on the system and the product in partnership with agent coding workflows. I recently built a Voice AI for older people. To build it I created an agentic engineering workflow (feel free to rip that up as I’m always looking to improve systems: https://ozbrain.com/resources/eng-flow) My approach with coding agents is trust but verify, and I’m trying to replace the parts where a human would review with an adversarial or specialized agent who would give a better answer/review. I have workflows that will go high level task to shipped PR running in Claude cloud sessions. I use Claude Code locally and Cursor when I want a tighter loop on doing visual work like UI or layout. And Codex to either load balance usage for TokenThriffting or when I want a different llm to think thru something. It was a pain in the ass passing .md files around and keep track of which version was the most recent, so I built a hosted .md storage right in Supabase and any of my agents already have Supabase access. This let me build a solid, scalable, secure voice AI from my phone at the gym. All my agents have access to our knowledge, can write to it, update and refer to it as we build and improve the product and the systems we use. Out of 75 founder friends I asked about how they manage shared knowledge, 26 built their own custom knowledge systems… Obsidian vaults with 7k files synced through a VPS, markdown repos behind their own MCP servers, cron jobs stitching Supabase to a skills file… each a different Frankenstein they have to maintain. 32 said they felt the pain of moving static files around but didn’t have any solution for it. So I rebuilt my brain better and used it to build it. // HOW YOU CAN HELP Would love to have you try it out. The maintenance loop is still in alpha so not running it on customer data yet. If you built your own brain I’d love to hear how you did it. What criteria was most important for you in its design & function. If you are tired of shuffling .md files around I’d love to have you try out OzBrain and to give feedback, just ask your agent to put it in the shared bugs & features brain! Cheers! Bubs.co