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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
Frugal Tokens
I wanted to share a project I’ve been working on called Frugal Tokens. I originally built it because I was curious to see how much all of my sessions cost and how much cache misses affected that spend. I’d noticed people had widely different spend profiles and wanted to better understand what might contribute to that. As I’ve worked on this, the tool has grown to show more usage patterns across all of your sessions. It shows overall usage, estimated working time and overlapping sessions, and where your spend is coming from across models and cache misses. I also have a few session level metrics with percentile breakdowns, along with a list of your sessions and high level info. Clicking into a session opens an explorer where you can see individual model calls and tool inputs and outputs. You can also jump directly to where a cache miss happened. There’s also a rough cost comparison that shows what the recorded session would have cost with another model’s pricing, or for Anthropic, with 5m vs 1h caching. In the future, I’d love to collect more information to see which patterns might make people’s workflows more expensive, e.g. long sessions, high context usage, many turns, etc. The tool requires deno, but is just one command to run once that is installed. The demo provided has some of the data scrubbed, but helps to show what it looks like before running it. Would appreciate any thoughts or ideas https://github.com/dpclark4/frugal-tokens
A sample dataset of computer-use tasks on professional software
Show HN: A sample dataset of computer-use tasks on professional software
Parley
Hi HN, I saw one friction point when working as part of a team that uses coding agents extensively - there is nothing to enable coordination between agent used by people in a team. Many times my agent would ask me to decide upon something with a fellow teammate, for which I have to serve as the network layer. So I built Parley where agents can connect to the hub over MCP with their own team-scoped token. An agent addresses a teammate's agent by name and ask questions/handover tasks. Agents can also use file claims to signal what files they are working on, to highlight overlapping work. Everything is recorded for audit. If an agent needs human decision/approval, it can ping over Slack/Telegram, and get replies over the same. The hard part was making an agent wake up from an idle session and start working, so I built an optional feature called Claude Live Wake. If the exact project session is already running, Parley can wake the idle Claude session using channels, and notify it that eligible work is waiting. Another challenge was trust- anything another agent sends has to be treated as untrusted input. Every message is tagged by origin - human, agent, or system. Message bodies only enter an agent when it explicitly fetches instead of injecting mid turn. These are specifically mentioned to be treated as string messages instead of prompts/commands.
Infrawrench
Show HN: Infrawrench – a tool to manage cloud and svcs with workflows and chat
FeyNoBg
Hey HN, I’m Shreyash from Feyn. We help companies build custom models from their data. Today, we’re releasing FeyNoBg, an automatic background removal model. Alongside it, we're open-sourcing NoBg, the Python library we built to train and run it. Try the model here: https://huggingface.co/spaces/feyninc/feynobg. Check out the library here: https://github.com/feyninc/nobg Some sample outputs: (1) Soccer Freekick: https://drive.google.com/file/d/1MZkAGLwbhNVOZ0Oi7XvpCfSEu9Q... (2) Hair in wind: https://drive.google.com/file/d/1Odc2m0XMVH9uZtvI_KjaRbXzhLL... (3) Bicycle with visible spokes: https://drive.google.com/file/d/1h99ahjfrtS1MFQJJgiKE2fuM3HZ... (4) Live Demo video: https://youtu.be/b1heHPvY8BM Background removal separates an image's subject from its surrounding. We've all tried it at some point. Often it is to reuse the subject in a different artifact. Nowadays, it is common to make chat stickers out of it. It is one of the most common but under-appreciated uses of AI. It is also surprisingly complex. Models can be easily confused by camouflage, motion blur, or fine structures like hair. The task requires two skills. First, a model has to identify the foreground. Second, it has to trace the foreground’s boundary and estimate an opacity value for each pixel. Generally, these skills are taught with different datasets. That creates a failure point. A poor training mix can improve one skill at the expense of the other. We saw this in our controlled evaluation. A training run with just the MaskFactory dataset improved on the CAMO benchmark but regressed on DIS5K. For FeyNoBg, we took an interpretability-first approach to training. We first studied how BiRefNet’s stages contribute to finding the foreground and reconstructing its boundary. We discovered that the third stage of it's feature extractor holds a lot of information. Both localization and boundary reconstruction depend heavily on the feature map produced here. This led us to expand this stage from 18 to 24 blocks while preserving the pre-trained weights. We then trained FeyNoBg on 26.1K diverse examples assembled from 10 datasets. The goal was to improve foreground identification and boundary precision without sacrificing either one. Across eight benchmarks, FeyNoBg achieves the best published score on four and comes within 2% of the leader on the rest. Building FeyNoBg also exposed a tooling problem. Image matting models are usually released as isolated repositories with incompatible preprocessing, training, and evaluation code. We built NoBg to solve this. NoBg puts these workflows behind one Python interface. It supports BiRefNet today, with more architectures coming. We hope you build something exciting with it! Happy to answer any questions!
Working Async
After living in multiple countries around the world and working remotely, I discovered that the best working cultures are asynchronous ones. An asynchronous working culture is a model where team members collaborate and complete tasks on their own schedules, without needing to be online at the same time or respond instantly. This means employees can work when and where they please for maximum productivity, often times companies who have succesfully implemented async communication have teams with employees across the globe. I created a job board dedicated to curating these types of jobs because I strongly believe that async communication will be a standard for many job types in the future. You can check out the site here: https://workingasync.io Right now there are over 400 jobs listed (including roles from companies like DuckDuckGo, GitLab, and Docker). I’m planning to add new features over the coming weeks, and have update the blog regularly. Feedback is very welcome!
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.
Plate
Show HN: Plate – The fastest way to run projects without becoming a PM tool
Clawemon, A Pokemon-style MMO for your agents
Team up with your agent to trade, battle, and collect every Clawemon! This has been a fun side project for us, and we had a great time playing it with friends over the weekend. We’ll keep adding more towns and expanding the game in our spare time. Let us know your thoughts and what you’d like to see next - thank you! Send your agent to clawemon.com and join the world of Clawemon!