The best Hacker News stories from Show from the past day
Latest posts:
Show HN: Ghost Pepper Meet local meeting transcription and diarization
100% local & private transcription engine for macOS. Captures & does speaker diarization. Originally was building as its own app, but can leverage same local models from my original push-to-talk voice transcription product so combined them into one app.
Show HN: Agent Vault – Open-source credential proxy and vault for agents
Hey HN! Today we're launching Agent Vault - an open source HTTP credential proxy and vault for AI agents. Repo is at <a href="https://github.com/Infisical/agent-vault" rel="nofollow">https://github.com/Infisical/agent-vault</a>, and there's an in-depth description at <a href="https://infisical.com/blog/agent-vault-the-open-source-credential-proxy-and-vault-for-agents">https://infisical.com/blog/agent-vault-the-open-source-crede...</a>.<p>We built Agent Vault in response to a question that been plaguing the industry: How do we give agents secure access to services without them reading any secrets?<p>Most teams building agents have run into this exact problem: They build an agent or agentic system and come to realize at some point that it needs credentials in order to access any services. The issue is that agents, unlike traditional workloads, are non-deterministic, highly-prone to prompt injection, and thus can easily be manipulated to leaking the credentials that they need to operate. This is the problem of credential exfiltration (not to be confused with data exfiltration).<p>In response to this, some teams we've seen have implemented basic guardrails and security controls to mitigate this risk in their agentic environments including using short-lived access tokens. The more advanced teams have started to converge toward a pattern: credential brokering, the idea being to separate agents from their credentials through some form of egress proxy. In this model, the agent makes a request to a proxy that attaches a credential onto it and brokers it through to the target service. This proxy approach is actually used in Anthropic's Managed Agents architecture blog with it being that "the harness is never made aware of the credentials." We've seen similar credential brokering schemes come out from Vercel and in Cloudflare's latest Outbound Workers.<p>Seeing all this made us think: What if we could create a portable credential brokering service plugged seamlessly into agents' existing workflows in an interface agnostic way, meaning that agents could continue to work with APIs, CLIs, SDKs, MCPs without interference and get the security of credential brokering.<p>This led to Agent Vault - an open source HTTP credential proxy and vault that we're building for AI agents. You can deploy this as a dedicated service and set up your agent's environment to proxy requests through it. Note that in a full deployment, you do need to lock down the network so that all outbound traffic is forced through Agent Vault<p>The Agent Vault (AV) implementation has a few interesting design decisions:<p>- Local Forward Proxy: AV chooses an interface agnostic approach to credential brokering by following a MITM architecture using HTTPS_PROXY as an environment variable set in the agent's environment to redirect traffic through it; this also means that it runs its own CA whose certificate must be configured on the client's trust store.<p>- MITM architecture: Since AV terminates TLS in order to do credential brokering its able to inspect traffic and apply rules to it before establishing a new TLS connection upstream. This makes it a great to be able to extend AV to incorporate firewall-like features to be applied at this proxy layer.<p>- Portable: AV itself is a single Go binary that bundles a server and the CLI; it can be deployed as a Docker container as well. In practice, this means that you can self-host AV on your own infrastructure and it should work more universally than provider specific approaches like that of Vercel and Cloudflare.<p>While the preliminary design of Agent Vault is a bit clunky to work with and we’d wished to have more time to smoothen the developer experience around it, particularly around the configuration setup for agents to start proxying requests through it, we figured it would be best to open source the technology and work with the community to make gradual improvements for it to work seamlessly across all agentic use cases since each has its own nuances.<p>All in all, we believe credential brokering is the right next step for how secrets management should be done for agents and would love to hear your views, questions, feedback!
Show HN: Tolaria – open-source macOS app to manage Markdown knowledge bases
Hey there! I am Luca, I write <a href="https://refactoring.fm/" rel="nofollow">https://refactoring.fm/</a> and I built Tolaria for myself to manage my own knowledge base (10K notes, 300+ articles written in over 6 years of newslettering) and work well with AI.<p>Tolaria is offline-first, file-based, has first-class support for git, and has strong opinions about how you should organize notes (types, relationships, etc).<p>Let me know your thoughts!
Show HN: Built a daily game where you sort historical events chronologically
Show HN: Honker – Postgres NOTIFY/LISTEN Semantics for SQLite
Show HN: WeTransfer Alternative for Developers
Show HN: Backlit Keyboard API for Python
It currently supports Linux as of now. You can use this package to tinker with many things. Let's say, if you want to make a custom notification system, like if your website is down, you can make a blink notification with it. MacOS support is underway. I haven't tested Windows yet, I don't use it anymore btw.
In future, if this package reaches nice growth, I'll be happy to make a similar Rust crate for it.
Show HN: Broccoli, one shot coding agent on the cloud
Hi HN — we built Broccoli, an open-source harness for taking coding tasks from Linear, running them in isolated cloud sandboxes, and opening PRs for a human to review.<p>We’re a small team, and our main company supplies voice data. But we kept running into the same problem with coding agents. We’d have a feature request, a refactor, a bug, and some internal tooling work all happening at once, and managing that through local agent sessions meant a lot of context switching, worktree juggling, and laptops left open just so tasks could keep running.<p>So we built Broccoli. Each task gets its own cloud sandbox to be executed end to end independently. Broccoli checks out the repo, uses the context in the ticket, works through an implementation, runs tests and review loops, and opens a PR for someone on the team to inspect.<p>Over the last four weeks, 100% of the PRs from non-developers are shipped via Broccoli, which is a safer and more efficient route. For developers on the team, this share is around 60%. More complicated features require more back and forth design with Codex / Claude Code and get shipped manually using the same set of skills locally.<p>Our implementation uses:<p>1. Webhook deployment: GCP
2. Sandbox: GCP or Blaxel
3. Project management: Linear
4. Code hosting & CI/CD: Github<p>Repo: <a href="https://github.com/besimple-oss/broccoli" rel="nofollow">https://github.com/besimple-oss/broccoli</a><p>We believe that if you should invest in your own coding harness if coding is an essential part of your business. That’s why we decided to open-source it as an alternative to all the cloud coding agents out there. Would love to hear your feedback on this!
Show HN: Broccoli, one shot coding agent on the cloud
Hi HN — we built Broccoli, an open-source harness for taking coding tasks from Linear, running them in isolated cloud sandboxes, and opening PRs for a human to review.<p>We’re a small team, and our main company supplies voice data. But we kept running into the same problem with coding agents. We’d have a feature request, a refactor, a bug, and some internal tooling work all happening at once, and managing that through local agent sessions meant a lot of context switching, worktree juggling, and laptops left open just so tasks could keep running.<p>So we built Broccoli. Each task gets its own cloud sandbox to be executed end to end independently. Broccoli checks out the repo, uses the context in the ticket, works through an implementation, runs tests and review loops, and opens a PR for someone on the team to inspect.<p>Over the last four weeks, 100% of the PRs from non-developers are shipped via Broccoli, which is a safer and more efficient route. For developers on the team, this share is around 60%. More complicated features require more back and forth design with Codex / Claude Code and get shipped manually using the same set of skills locally.<p>Our implementation uses:<p>1. Webhook deployment: GCP
2. Sandbox: GCP or Blaxel
3. Project management: Linear
4. Code hosting & CI/CD: Github<p>Repo: <a href="https://github.com/besimple-oss/broccoli" rel="nofollow">https://github.com/besimple-oss/broccoli</a><p>We believe that if you should invest in your own coding harness if coding is an essential part of your business. That’s why we decided to open-source it as an alternative to all the cloud coding agents out there. Would love to hear your feedback on this!
Show HN: Ctx – a /resume that works across Claude Code and Codex
ctx is a local SQLite-backed skill for Claude Code and Codex that stores context as a persistent workstream that can be continued across agent sessions. Each workstream can contain multiple sessions, notes, decisions, todos, and resume packs. It essentially functions as a /resume that can work across coding agents.<p>Here is a video of how it works: <a href="https://www.loom.com/share/5e558204885e4264a34d2cf6bd488117" rel="nofollow">https://www.loom.com/share/5e558204885e4264a34d2cf6bd488117</a><p>I initially built ctx because I wanted to try a workstream that I started on Claude and continue it from Codex. Since then, I’ve added a few quality of life improvements, including the ability to search across previous workstreams, manually delete parts of the context with, and branch off existing workstreams.. I’ve started using ctx instead of the native ‘/resume’ in Claude/Codex because I often have a lot of sessions going at once, and with the lists that these apps currently give, it’s not always obvious which one is the right one to pick back up. ctx gives me a much clearer way to organize and return to the sessions that actually matter.<p>It’s simple to install after you clone the repo with one line: ./setup.sh, which adds the skill to both Claude Code and Codex. After that, you should be able to directly use ctx in your agent as a skill with ‘/ctx [command]’ in Claude and ‘ctx [command]’ in Codex.<p>A few things it does:<p>- Resume an existing workstream from either tool<p>- Pull existing context into a new workstream<p>- Keep stable transcript binding, so once a workstream is linked to a Claude or Codex conversation, it keeps following that exact session instead of drifting to whichever transcript file is newest<p>- Search for relevant workstreams<p>- Branch from existing context to explore different tasks in parallel<p>It’s intentionally local-first: SQLite, no API keys, and no hosted backend. I built it mainly for myself, but thought it would be cool to share with the HN community.
Show HN: Ctx – a /resume that works across Claude Code and Codex
ctx is a local SQLite-backed skill for Claude Code and Codex that stores context as a persistent workstream that can be continued across agent sessions. Each workstream can contain multiple sessions, notes, decisions, todos, and resume packs. It essentially functions as a /resume that can work across coding agents.<p>Here is a video of how it works: <a href="https://www.loom.com/share/5e558204885e4264a34d2cf6bd488117" rel="nofollow">https://www.loom.com/share/5e558204885e4264a34d2cf6bd488117</a><p>I initially built ctx because I wanted to try a workstream that I started on Claude and continue it from Codex. Since then, I’ve added a few quality of life improvements, including the ability to search across previous workstreams, manually delete parts of the context with, and branch off existing workstreams.. I’ve started using ctx instead of the native ‘/resume’ in Claude/Codex because I often have a lot of sessions going at once, and with the lists that these apps currently give, it’s not always obvious which one is the right one to pick back up. ctx gives me a much clearer way to organize and return to the sessions that actually matter.<p>It’s simple to install after you clone the repo with one line: ./setup.sh, which adds the skill to both Claude Code and Codex. After that, you should be able to directly use ctx in your agent as a skill with ‘/ctx [command]’ in Claude and ‘ctx [command]’ in Codex.<p>A few things it does:<p>- Resume an existing workstream from either tool<p>- Pull existing context into a new workstream<p>- Keep stable transcript binding, so once a workstream is linked to a Claude or Codex conversation, it keeps following that exact session instead of drifting to whichever transcript file is newest<p>- Search for relevant workstreams<p>- Branch from existing context to explore different tasks in parallel<p>It’s intentionally local-first: SQLite, no API keys, and no hosted backend. I built it mainly for myself, but thought it would be cool to share with the HN community.
Scoring Show HN submissions for AI design patterns
Show HN: Holos – QEMU/KVM with a compose-style YAML, GPUs and health checks
I got tired of libvirt XML and Vagrant's Ruby/reload dance for single-host VM stacks, so I built a compose-style runtime directly on QEMU/KVM.<p>What's there: GPU passthrough as a first-class primitive (VFIO, OVMF, per-instance EFI vars), healthchecks that gate depends_on over SSH, socket-multicast L2 between VMs with no root and no bridge config, cloud-init wired through the YAML, Dockerfile support for provisioning.<p>What it's not: Kubernetes. No clustering, no live migration, no control plane. Single host.
Prototype, but I'm running it on real hardware. Curious what breaks for people.
Show HN: Holos – QEMU/KVM with a compose-style YAML, GPUs and health checks
I got tired of libvirt XML and Vagrant's Ruby/reload dance for single-host VM stacks, so I built a compose-style runtime directly on QEMU/KVM.<p>What's there: GPU passthrough as a first-class primitive (VFIO, OVMF, per-instance EFI vars), healthchecks that gate depends_on over SSH, socket-multicast L2 between VMs with no root and no bridge config, cloud-init wired through the YAML, Dockerfile support for provisioning.<p>What it's not: Kubernetes. No clustering, no live migration, no control plane. Single host.
Prototype, but I'm running it on real hardware. Curious what breaks for people.
Show HN: Daemons – we pivoted from building agents to cleaning up after them
For almost two years, we've been developing Charlie, a coding agent that is autonomous, cloud-based, and focused primarily on TypeScript development. During that time, the explosion in growth and development of LLMs and agents has surpassed even our initially very bullish prognosis. When we started Charlie, we were one of the only teams we knew fully relying on agents to build all of our code. We all know how that has gone — the world has caught up, but working with agents hasn't been all kittens and rainbows, especially for fast moving teams.<p>The one thing we've noticed over the last 3 months is that the more you use agents, the more work they create. Dozens of pull requests means older code gets out of date quickly. Documentation drifts. Dependencies become stale. Developers are so focused on pushing out new code that this crucial work falls through the cracks. That's why we pivoted away from agents and invented what we think is the necessary next step for AI powered software development.<p>Today, we're introducing Daemons: a new product category built for teams dealing with operational drag from agent-created output. Named after the familiar background processes from Linux, Daemons are added to your codebase by adding an .md file to your repo, and run in a set-it-and-forget-it way that will make your lives easier and accelerate any project. For teams that use Claude, Codex, Cursor, Cline, or any other agent, we think you'll really enjoy what Daemons bring to the table.
Show HN: Daemons – we pivoted from building agents to cleaning up after them
For almost two years, we've been developing Charlie, a coding agent that is autonomous, cloud-based, and focused primarily on TypeScript development. During that time, the explosion in growth and development of LLMs and agents has surpassed even our initially very bullish prognosis. When we started Charlie, we were one of the only teams we knew fully relying on agents to build all of our code. We all know how that has gone — the world has caught up, but working with agents hasn't been all kittens and rainbows, especially for fast moving teams.<p>The one thing we've noticed over the last 3 months is that the more you use agents, the more work they create. Dozens of pull requests means older code gets out of date quickly. Documentation drifts. Dependencies become stale. Developers are so focused on pushing out new code that this crucial work falls through the cracks. That's why we pivoted away from agents and invented what we think is the necessary next step for AI powered software development.<p>Today, we're introducing Daemons: a new product category built for teams dealing with operational drag from agent-created output. Named after the familiar background processes from Linux, Daemons are added to your codebase by adding an .md file to your repo, and run in a set-it-and-forget-it way that will make your lives easier and accelerate any project. For teams that use Claude, Codex, Cursor, Cline, or any other agent, we think you'll really enjoy what Daemons bring to the table.
Show HN: Mediator.ai – Using Nash bargaining and LLMs to systematize fairness
Eight years ago, my then-fiancée and I decided to get a prenup, so we hired a local mediator. The meetings were useful, but I felt there was no systematic process to produce a final agreement. So I started to think about this problem, and after a bit of research, I discovered the Nash bargaining solution.<p>Yet if John Nash had solved negotiation in the 1950s, why did it seem like nobody was using it today? The issue was that Nash's solution required that each party to the negotiation provide a "utility function", which could take a set of deal terms and produce a utility number. But even experts have trouble producing such functions for non-trivial negotiations.<p>A few years passed and LLMs appeared, and about a year ago I realized that while LLMs aren’t good at directly producing utility estimates, they are good at doing comparisons, and this can be used to estimate utilities of draft agreements.<p>This is the basis for Mediator.ai, which I soft-launched over the weekend. Be interviewed by an LLM to capture your preferences and then invite the other party or parties to do the same. These preferences are then used as the fitness function for a genetic algorithm to find an agreement all parties are likely to agree to.<p>An article with more technical detail: <a href="https://mediator.ai/blog/ai-negotiation-nash-bargaining/" rel="nofollow">https://mediator.ai/blog/ai-negotiation-nash-bargaining/</a>
Show HN: Mediator.ai – Using Nash bargaining and LLMs to systematize fairness
Eight years ago, my then-fiancée and I decided to get a prenup, so we hired a local mediator. The meetings were useful, but I felt there was no systematic process to produce a final agreement. So I started to think about this problem, and after a bit of research, I discovered the Nash bargaining solution.<p>Yet if John Nash had solved negotiation in the 1950s, why did it seem like nobody was using it today? The issue was that Nash's solution required that each party to the negotiation provide a "utility function", which could take a set of deal terms and produce a utility number. But even experts have trouble producing such functions for non-trivial negotiations.<p>A few years passed and LLMs appeared, and about a year ago I realized that while LLMs aren’t good at directly producing utility estimates, they are good at doing comparisons, and this can be used to estimate utilities of draft agreements.<p>This is the basis for Mediator.ai, which I soft-launched over the weekend. Be interviewed by an LLM to capture your preferences and then invite the other party or parties to do the same. These preferences are then used as the fitness function for a genetic algorithm to find an agreement all parties are likely to agree to.<p>An article with more technical detail: <a href="https://mediator.ai/blog/ai-negotiation-nash-bargaining/" rel="nofollow">https://mediator.ai/blog/ai-negotiation-nash-bargaining/</a>
Show HN: Mediator.ai – Using Nash bargaining and LLMs to systematize fairness
Eight years ago, my then-fiancée and I decided to get a prenup, so we hired a local mediator. The meetings were useful, but I felt there was no systematic process to produce a final agreement. So I started to think about this problem, and after a bit of research, I discovered the Nash bargaining solution.<p>Yet if John Nash had solved negotiation in the 1950s, why did it seem like nobody was using it today? The issue was that Nash's solution required that each party to the negotiation provide a "utility function", which could take a set of deal terms and produce a utility number. But even experts have trouble producing such functions for non-trivial negotiations.<p>A few years passed and LLMs appeared, and about a year ago I realized that while LLMs aren’t good at directly producing utility estimates, they are good at doing comparisons, and this can be used to estimate utilities of draft agreements.<p>This is the basis for Mediator.ai, which I soft-launched over the weekend. Be interviewed by an LLM to capture your preferences and then invite the other party or parties to do the same. These preferences are then used as the fitness function for a genetic algorithm to find an agreement all parties are likely to agree to.<p>An article with more technical detail: <a href="https://mediator.ai/blog/ai-negotiation-nash-bargaining/" rel="nofollow">https://mediator.ai/blog/ai-negotiation-nash-bargaining/</a>
Show HN: GoModel – an open-source AI gateway in Go
Hi, I’m Jakub, a solo founder based in Warsaw.<p>I’ve been building GoModel since December with a couple of contributors. It's an open-source AI gateway that sits between your app and model providers like OpenAI, Anthropic or others.<p>I built it for my startup to solve a few problems:<p><pre><code> - track AI usage and cost per client or team
- switch models without changing app code
- debug request flows more easily
- reduce AI spendings with exact and semantic caching
</code></pre>
How is it different?<p><pre><code> - ~17MB docker image
- LiteLLM's image is more than 44x bigger ("docker.litellm.ai/berriai/litellm:latest" ~ 746 MB on amd64)
- request workflow is visible and easy to inspect
- config is environment-variable-first by default
</code></pre>
I'm posting now partly because of the recent LiteLLM supply-chain attack. Their team handled it impressively well, but some people are looking at alternatives anyway, and GoModel is one.<p>Website: <a href="https://gomodel.enterpilot.io" rel="nofollow">https://gomodel.enterpilot.io</a><p>Any feedback is appreciated.