Why it's appearing sorry message when I trying to post?
2 by Edymilson | 0 comments on Hacker News.
Hack Nux
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New Show Hacker News story: Show HN: OzBrain, a shared brain for knowledge between agents and your team
Show HN: OzBrain, a shared brain for knowledge between agents and your team
9 by dariusmonsef | 2 comments on Hacker News.
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://ift.tt/gJSQ8nN ) 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
9 by dariusmonsef | 2 comments on Hacker News.
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://ift.tt/gJSQ8nN ) 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
New Show Hacker News story: Show HN: Zroar – Serialized Roaring Bitmaps in Zig
Show HN: Zroar – Serialized Roaring Bitmaps in Zig
3 by mrjn | 0 comments on Hacker News.
zroar is a ground-up implementation of Roaring Bitmaps data structure in Zig. zroar stores both the keys and (array, bitmap) containers in a single flat byte buffer, making the in-memory representation equal to the on-disk or over-the-network representation, eliminating the serialization/deserialization step entirely. The design was originally aimed at systems which keep their posting lists on disk, but zroar performs faster than CRoaring even for purely in-memory ops, due to CPU cache locality. Against CRoaring 5.0's benchmarking suite (ported to Zig), zroar is faster in 339 out of 360 tests, being 2x-9x faster (geometric mean), and up to 600x faster on serialize/deserialize. zroar avoids complex mechanism (like adaptive radix trees), uses Zig native SIMD ops and is simpler. The main logic is written in ~2000 lines of code, while CRoaring's 64-bit bitmap codebase is over 17000 LOC. Not yet: By choice, zroar doesn't support run containers, and is 64-bit only. There are more details in the GitHub README. Try it out! I'd love feedback on the API and design. zroar is a Zig-based successor to my other project, sroar in Go, which showed a similar boost. So, I think this design should show performance gains in any language.
3 by mrjn | 0 comments on Hacker News.
zroar is a ground-up implementation of Roaring Bitmaps data structure in Zig. zroar stores both the keys and (array, bitmap) containers in a single flat byte buffer, making the in-memory representation equal to the on-disk or over-the-network representation, eliminating the serialization/deserialization step entirely. The design was originally aimed at systems which keep their posting lists on disk, but zroar performs faster than CRoaring even for purely in-memory ops, due to CPU cache locality. Against CRoaring 5.0's benchmarking suite (ported to Zig), zroar is faster in 339 out of 360 tests, being 2x-9x faster (geometric mean), and up to 600x faster on serialize/deserialize. zroar avoids complex mechanism (like adaptive radix trees), uses Zig native SIMD ops and is simpler. The main logic is written in ~2000 lines of code, while CRoaring's 64-bit bitmap codebase is over 17000 LOC. Not yet: By choice, zroar doesn't support run containers, and is 64-bit only. There are more details in the GitHub README. Try it out! I'd love feedback on the API and design. zroar is a Zig-based successor to my other project, sroar in Go, which showed a similar boost. So, I think this design should show performance gains in any language.
New Show Hacker News story: Show HN: Traccia - Observability, Runtime Control & Audit for agents
Show HN: Traccia - Observability, Runtime Control & Audit for agents
2 by vijaypoudel | 0 comments on Hacker News.
AI applications are becoming agents, which has started to take autonomous decisions. There are plenty of tools and platform available to trace, and observe what an agent or llms calls does. They are good in what they do, but tracing and observability isnt enough for AI agents era. We need a solution that can help you observe, evaluate, create run time policies to govern and finally audit the actions of the agent. We built Traccia to solve this problem. The good part, all of these can be achieved by just writing few lines of code. Traccia has an open-sourced sdk that can work with your existing observability tool like grafana, tempo, jaeger, etc. In case you need more than just observability, Traccia provides the platform to evaluate, control and audit the agents. The platform is easy to use. The product's documentation is quite extensive. It is also cloud vendor and framework agnostic. Traccia is being built by an Indian start up ,based out of Bengaluru. We are running a 3 months free trials so that you can explore without any strings attached. We are open to improvise and get better so please drop your comments and feedbacks.
2 by vijaypoudel | 0 comments on Hacker News.
AI applications are becoming agents, which has started to take autonomous decisions. There are plenty of tools and platform available to trace, and observe what an agent or llms calls does. They are good in what they do, but tracing and observability isnt enough for AI agents era. We need a solution that can help you observe, evaluate, create run time policies to govern and finally audit the actions of the agent. We built Traccia to solve this problem. The good part, all of these can be achieved by just writing few lines of code. Traccia has an open-sourced sdk that can work with your existing observability tool like grafana, tempo, jaeger, etc. In case you need more than just observability, Traccia provides the platform to evaluate, control and audit the agents. The platform is easy to use. The product's documentation is quite extensive. It is also cloud vendor and framework agnostic. Traccia is being built by an Indian start up ,based out of Bengaluru. We are running a 3 months free trials so that you can explore without any strings attached. We are open to improvise and get better so please drop your comments and feedbacks.
New Show Hacker News story: Show HN: Praxos – team messaging with built-in memory
Show HN: Praxos – team messaging with built-in memory
5 by mogusian | 1 comments on Hacker News.
Hey HN! What does a regular workflow at a startup look like? Probably like this: > One founder talks to customers > Another founder builds > A customer asks for something. The first founder explains it to the second. The second founder explains it to Claude > Two weeks any one of the founders asks one of the following questions: \* Why are we building it this way? \* What happened here? \* What did we promise ACME we’d do? \* How far along are we on X? > Then you scream on the inside because you have to figure out what happened... Before agents, the person who did one part of the work usually held a mental map. They could share the context with other people. Increasingly, they can’t. Small teams can run far more work in parallel than ever before thanks to AI, but someone now has to connect all that context manually. The reasoning behind one product change might now be scattered across a customer call, two emails, a Slack thread, and four Claude conversations. Praxos is a team messaging app. It has channels, groups and threads like you’d expect. However, places where work happens—Github, harnesses, email, calls—get linked and converted into a living record of your company. This means everyone is always up to date with the latest developments. Agents can join conversations and inherit the same context humans do. Here are examples we’ve seen in production: You talk to a customer. Later, when an engineer or coding agent works on the feature, your conversation and the customer’s information are already in context. A new engineer picks up an existing project. They have immediate access to info about who worked on what part of the code base, the deltas across changes, and a granular view of their conversations with coding agents. There’s no need to interrogate the CTO. Three weeks later, someone asks why the engineer made a certain decision about the feature. Praxos fetches the engineer’s convos, the original feature request and call logs between the founder and the engineer. Then it contextualizes the work. We’re early, but the core product already works and is in use. You get messaging, search, memory and connectors. You can bring your own agents, use ours, or both. A desktop app is going live next week. We’re building native calls and mobile apps, so that all the communications that go into a company’s history can happen on Praxos. If you’re a 2 to 10 person team where one person is spending all day with customers while the rest of the team is shipping with Claude/Codex, we’d love to personally onboard you. Especially if you’ve already caught yourself asking “what did the customer want?” or “what’s going on with X?” last week. The first 10 teams to sign up get Praxos for free for 6 months. Email lucas@praxos.ai to get started!
5 by mogusian | 1 comments on Hacker News.
Hey HN! What does a regular workflow at a startup look like? Probably like this: > One founder talks to customers > Another founder builds > A customer asks for something. The first founder explains it to the second. The second founder explains it to Claude > Two weeks any one of the founders asks one of the following questions: \* Why are we building it this way? \* What happened here? \* What did we promise ACME we’d do? \* How far along are we on X? > Then you scream on the inside because you have to figure out what happened... Before agents, the person who did one part of the work usually held a mental map. They could share the context with other people. Increasingly, they can’t. Small teams can run far more work in parallel than ever before thanks to AI, but someone now has to connect all that context manually. The reasoning behind one product change might now be scattered across a customer call, two emails, a Slack thread, and four Claude conversations. Praxos is a team messaging app. It has channels, groups and threads like you’d expect. However, places where work happens—Github, harnesses, email, calls—get linked and converted into a living record of your company. This means everyone is always up to date with the latest developments. Agents can join conversations and inherit the same context humans do. Here are examples we’ve seen in production: You talk to a customer. Later, when an engineer or coding agent works on the feature, your conversation and the customer’s information are already in context. A new engineer picks up an existing project. They have immediate access to info about who worked on what part of the code base, the deltas across changes, and a granular view of their conversations with coding agents. There’s no need to interrogate the CTO. Three weeks later, someone asks why the engineer made a certain decision about the feature. Praxos fetches the engineer’s convos, the original feature request and call logs between the founder and the engineer. Then it contextualizes the work. We’re early, but the core product already works and is in use. You get messaging, search, memory and connectors. You can bring your own agents, use ours, or both. A desktop app is going live next week. We’re building native calls and mobile apps, so that all the communications that go into a company’s history can happen on Praxos. If you’re a 2 to 10 person team where one person is spending all day with customers while the rest of the team is shipping with Claude/Codex, we’d love to personally onboard you. Especially if you’ve already caught yourself asking “what did the customer want?” or “what’s going on with X?” last week. The first 10 teams to sign up get Praxos for free for 6 months. Email lucas@praxos.ai to get started!
New ask Hacker News story: Ask HN: How did you write code on restricted hardware?
Ask HN: How did you write code on restricted hardware?
2 by diselody | 2 comments on Hacker News.
I remember trying to write code for a project in college from those library computers when I did not have a laptop of my own at the time. I often used sites like Codepen and copy-pasted the progress into a github repo so I didnt lose my work. What are some workarounds you’ve used (or tried) for writing or running code from restricted hardware, like a school computer or a mobile device? What kind of project was it for, and what hurdles did you encounter?
2 by diselody | 2 comments on Hacker News.
I remember trying to write code for a project in college from those library computers when I did not have a laptop of my own at the time. I often used sites like Codepen and copy-pasted the progress into a github repo so I didnt lose my work. What are some workarounds you’ve used (or tried) for writing or running code from restricted hardware, like a school computer or a mobile device? What kind of project was it for, and what hurdles did you encounter?