The best Hacker News stories from Show from the past day
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Show HN: DeepSQL – A self-hostable DBA agent for Postgres and MySQL
Hi HN - I'm Venkat, founder of Stayflexi (YC), CMU CS grad and Ex-Oracle Query Engine team (patents in core databases)<p>DeepSQL started as an internal tool to stop our own databases from becoming the bottleneck they were becoming (13,000+ hotels in production). It worked well enough that we're releasing it.<p>DeepSQL is an AI agent that operates a database the way a senior DBA and Data Engineer would<p>1. Fixes slow queries (we cutdown DB spend by 4x)
2. Fixes DB bloat (blocks unnecessary schema changes, in vibecoded setup)
2. BI dashboards(we removed spend on tableau, retool and appsmith)
3. Security (We could redact access to sensitive, PII data to our employees) while engaging everyone in the org to interact with our database<p>How it works
1. Deepsql learns your data and relationships from your code base, rules and query logs
2. Deepsql agent has 20 background jobs that continuously monitor the schema changes, bottlenecks, and come up with solutions before the problem arises. (Schema bloat issues are irreversible)
3. Deepsql Brain has CLI and MCP surfaces that can directly work with Claude / Codex and Cursor.
4. Deepsql digest delivers the daily database health report.
5. Your team can connect to deepsql with their favourate surface areas - Web UI, CLI, MCP or Slack.<p>One line install:
curl -fsSL <a href="https://install.deepsql.ai/install.sh" rel="nofollow">https://install.deepsql.ai/install.sh</a> | bash<p>For community support:
<a href="https://discord.gg/duEJq7AeeG" rel="nofollow">https://discord.gg/duEJq7AeeG</a><p>Or just email me for expert level setup guidance
venkat@deepsql.ai
Show HN: OneCLI – OSS credential gateway that keeps secrets out of AI agents
hey HN, Jonathan and Guy here, creators of OneCLI (<a href="https://onecli.sh/">https://onecli.sh/</a>). OneCLI is an open source vault for AI Agents.<p>Traditional vaults are used to store your secrets and, on demand, provide them to you all in a secure way, trusting the person to keep them safe. We figured that in the agent's world, this is not the case, as you don't know what happens with the secret after it's delivered to the agent, or where it was saved. Or maybe someone even manipulated them to hand them over...<p>From that understanding, we decided to build a network gateway that sits between your AI agents and the services they call. OneCLI matches the request by host/path, verifies the agent should have access, swaps the placeholder for the real credential, and forwards the request.
the secrets set inside the OneCLI vault, encrypted on rest, or could fetch in realtime from your bitwarden / 1password wallets.<p>Demo - <a href="https://www.youtube.com/watch?v=5e5pbPEzZfY" rel="nofollow">https://www.youtube.com/watch?v=5e5pbPEzZfY</a>.<p>We started working on this by accident, even though our careers were in the security space. We were working on a devtool called ChartDB, an open-source DB tool. When OpenClaw took off back in January, we started using it to orchestrate agents on top of ChartDB. We quickly understood there is a big issue around auth. Agents need credentials to do real work, but to give them those secrets would not be the best idea. they keep them in their memory and also write them down to local files and their sessions as plain text. And we knew that agents can easily be fooled into giving up those API keys/secrets. So we needed some way to control the agent and stop prompt injections from tricking it into using its services for an attacker's benefit. Not providing the keys to the agent + adding alerts or human-in-the-loop for sensitive operations, in case someone manipulates the agent, and access logs are being audited.<p>We use it ourselves daily. My OpenClaw runs my day across Attio, Gmail, and my calendar, with human-in-the-loop approval on sensitive actions. The approval is enforced at the network layer, so it holds whether the agent goes through MCP, CLI, curl, or code it wrote on the fly. Guy uses it to review PRs but can't merge without his approval.<p>Companies mostly use it for coding agents currently, which get creative about finding ways to elevate their permissions to reach the goal. With OneCLI, it doesn't matter what the agent finds: if the request isn't in policy, it will not go through.<p>The proxy is written in Rust, the dashboard is Next.js, and secrets are AES-256-GCM encrypted at rest. Everything runs in a Docker container. It works with any agent framework (Claude Code, Codex, Cursor, OpenClaw, Hermes or anything that can set an HTTPS_PROXY).<p>It won't stop an agent from misusing access it legitimately has, so scope policies tightly!<p>happy to answer anything. We believe we can't trust the model to behave and have to set deterministic rules to stay in control. Comments welcome!
Show HN: Claude-thermos keeps your Claude session warm for you
Show HN: Remux – an open-source tmux workspace designed for iPhone
Show HN: Palmier Pro – Open-source macOS video editor built for AI
Hi HN, we are Marcos and Harrison, cofounders of Palmier (<a href="https://palmier.io">https://palmier.io</a>). We are building Palmier Pro, an open source macOS video editor, with built-in AI generation and a local MCP server that connects to your agent. Here are a few demos:<p>- Making some AI transitions: <a href="https://www.youtube.com/watch?v=hbM_-eR1GX4" rel="nofollow">https://www.youtube.com/watch?v=hbM_-eR1GX4</a><p>- Multicam editing with Codex: <a href="https://www.youtube.com/watch?v=SjS2q2LT1q8" rel="nofollow">https://www.youtube.com/watch?v=SjS2q2LT1q8</a><p>- Cutting long form clips into shorts: <a href="https://www.youtube.com/watch?v=PR66eN2ouuQ" rel="nofollow">https://www.youtube.com/watch?v=PR66eN2ouuQ</a><p>We built Palmier Pro as an internal tool when we were making AI launch videos for other startups. The main problem it solved in the beginning was the back-and-forth between AI generation platform and video editor. The iteration loop was awkward: AI videos → download → import to editor → edit → realize we need to change the AI video → repeat. So we built a minimal video editor where we could let Claude generate AI videos inside the editor.<p>As we gave more and more tools to the agent, we wanted to push to see what else agents can do in the video editing space. So today, your Claude/Codex can:<p>- Manage projects inside Palmier Pro<p>- Import media from a public URL or filesystem to the project, and organize them in folders<p>- Search media (by embedding footages using SigLIP2 running locally)<p>- Edit the timeline (tracks/clips/keyframes operations)<p>- Generate images, videos, sound effects, captions, music<p>- Export videos<p>There are two ways for LLMs to interact with the editor: by connecting to the local MCP server, or using the in-app chat. Both use the same tools and APIs exposed by the video editor.<p>We have seen people using MCP server to connect to their own workflow to automate massive-scale video editing (e.g. given this same podcast style, replicate it with other footages that I have). We have also seen people using the in-app chat where it lives closer to the editor UI, with lower latency for faster iteration.<p>We don't believe that AI is going to replace human creativity (nor should it), but where it can really help is in automating the gruntwork in video editing. Most work in the editing workflow is more mechanical than creative. Our vision is to build a video editor where AI can enable more individuals to create.<p>Throughout our experiments, AI is not very good at creative editing, but given a pattern (transcription-based, beat-based), it can do a decent job at rough cut.<p>We built Palmier Pro in Swift as an MVP because we wanted better performance and minimal dependencies (no nodejs/webview), with some native macOS APIs like SpeechAnalyzer and CoreML for us to run some models locally. We use SpeechAnalyzer for local transcriber, SigLip2 to embed video frames, beat_this for beat detection, Silero VAD for silence detection, all running locally. The tradeoff is that we don't support Linux or Windows at the moment.<p>Palmier Pro is open source and free to try out (macOS 26 only at the moment, though - we'll support more platforms over time, but for now are focusing on iterating the core product).<p>No login required except for the AI generation features, which route requests to our backend. We are offering free credits on sign up so you can try out the AI generation as well.<p>We'd love to hear your feedback!
Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models
I’ve been building Echo (<a href="https://echo.tracerml.ai/" rel="nofollow">https://echo.tracerml.ai/</a>), an experiment in making one AI system out of a pool of open-weight models rather than choosing a single model and using it for every task.<p>It started with a simple experiment. I took a group of models, including GLM-5.2, Kimi K2.7 and others, and ran them on the same evaluations. Then I measured what would happen if, for each problem, you somehow knew in advance which models would be useful and how their outputs should be combined.<p>That hypothetical system performed substantially better than any individual model in the pool. Of course, it is not something you can actually deploy because it relies on knowing which decisions were good after seeing the result. Echo is my attempt to recover some of that advantage without having that information in advance.<p>For each request, Echo decides how much computation to allocate, which models should participate, and how their work should be combined. Some prompts may only need a relatively small amount of inference, while others benefit from multiple models working on different parts of the problem.<p>One thing that surprised me while building it was how complementary the models are. A model that is clearly weaker overall can still be extremely useful on particular problems or as part of a combination.<p>On my first evaluation mix, Echo consistently performed better than the best individual model in its pool. It also reached roughly the same aggregate result as Fable, which I used as one of the stronger comparison systems, at around one third of the inference cost.<p>There are still some cases where Echo makes the wrong allocation or combination decision. I’m currently spending a lot of time understanding those failures, as well as testing whether the same approach holds up on coding and agentic tasks where measuring the quality of each decision becomes much harder.<p>I built a chat interface (echo.tracerml.ai) and an OpenAI-compatible API (<a href="https://echo.tracerml.ai/docs/api" rel="nofollow">https://echo.tracerml.ai/docs/api</a>) so the system can be tested outside the evaluation setup.<p>Here is a short/high level video on how it works: <a href="https://www.youtube.com/watch?v=lJFJSvOdXhg" rel="nofollow">https://www.youtube.com/watch?v=lJFJSvOdXhg</a><p>I wrote up the evaluation methodology, individual model results, costs and current limitations here: <a href="https://echo.tracerml.ai/eval" rel="nofollow">https://echo.tracerml.ai/eval</a><p>I would love for you to try it! Especially if you hit any weird failure cases or places where the allocation looks unintuitive.
Show HN: Cactus Hybrid: We taught Gemma 4 to know when it's wrong
Hey HN, Henry & Roman here from Cactus.<p>A small, on-device model is fast and private, but sometimes wrong, but frontier models are getting expensive pretty fast. So, we post-trained Gemma 4 E2B post-trained to know when it's wrong. Every response comes with a confidence score between 0 and 1. Developers can accept the on-device when it's high, hand off to a bigger cloud model when it's low. By routing only 15-35% of queries to Gemini 3.1 Flash-Lite, Gemma-4-E2B matches Gemini 3.1 Flash-Lite on most benchmarks.<p>- ChartQA: 15-20%<p>- LibriSpeech: 25-30%<p>- MMBench, GigaSpeech, MMAU: 30-35%<p>- MMLU-Pro: 45-55%<p>We were always frustrated by the routing signals hybrid apps rely on: asking the model to rate itself in text (unreliable, and you're parsing prose), or token entropy heuristics (barely better than a coin flip in our tests). So we did mechanistic studies on small models, Gemma 4 particularly, and found the hidden state for different layers carry meaningful self-awareness signal for various situations.<p>SO we extended the model with a 68k params probe layer (LayerNorm, low-rank projection, attention pooling, small MLP head) reads one intermediate layer during decoding and predicts p(wrong); confidence = 1 - p(wrong), returned as structured data, never parsed out of the answer text.<p>Across 12 hold-out benchmarks spanning text, vision and audio, the probe averages 0.814 AUROC vs 0.549 for token entropy. The result that convinced us this is real: the probe was trained on zero audio data, yet scores 0.79-0.88 AUROC on four audio benchmarks where entropy is near-random or worse (0.32-0.52). It's reading a modality-independent correctness signal from the hidden state, not memorizing patterns from its training data.<p>We published all weights on HuggingFace and provide copy-pase codes to run it on Transformers, MLX, Llama.cpp or Cactus. With Ollama, vLLM, SGLang etc in the works. For llama.cpp we ship a patch series you compile in once (upstreaming is planned). The code is MIT licensed; Gemma model use remains subject to the Gemma terms.<p>GitHub: <a href="https://github.com/cactus-compute/cactus-hybrid" rel="nofollow">https://github.com/cactus-compute/cactus-hybrid</a><p>Weights: <a href="https://huggingface.co/collections/Cactus-Compute/cactus-hybrid-6a60da4551074db058e8bb64" rel="nofollow">https://huggingface.co/collections/Cactus-Compute/cactus-hyb...</a><p>Some caveats:<p>- The probe scores single-sequence decoding only, up to the first 1024 generated tokens.<p>- Handoff works best when routing per task in a multi-step process, not per step.<p>- Hierarchical routing is still in the works: try on-device, then DeepSeek v4 Flash, before Fable/GPT5.5/Gemini/Muse/Grok.<p>- The technique is boutique for each model, we will share each weights as they roll out.<p>These issues are currently being tackled at Cactus and updated weights will be shipped directly into the HuggingFace collection and GitHub repository straight up. Please let us know your thoughts, it helps us find ways to improve the design progressively.<p>Thanks a million!
Show HN: Web swing through midtown NYC
Show HN: Web swing through midtown NYC
Show HN: A new kind of FPS aim trainer
I played a lot of Valorant and got mad, so I made an aim trainer that analyzes your raw crosshair movement to explore your raw motor and perceptual weaknesses instead of scoring scenarios.
It also chooses sens and difficulty as part of the tasks, and makes playlists that are optimal difficulty for you to learn and progress faster!
Show HN: ReadKinetic – a free, local-first speed reader for your own books
Show HN: ReadKinetic – a free, local-first speed reader for your own books
Show HN: HN Hall of Fame – browse 3,100 legendary Hacker News links
Show HN: HN Hall of Fame – browse 3,100 legendary Hacker News links
Show HN: Bento - An entire PowerPoint in one HTML file (edit+view+data+collab)
Over the past few months, our team has been building more and more slidedecks using web frontend technologies with coding harnesses like Claude Code, but a common complaint is to make even small edits we need to edit the code either manually or via the harness.<p>To avoid this loop, I ended up creating Bento, a single HTML file with everything you need in a slide tool including animations and shared editing. There's no install or cloud login, everything works offline. The default deck is around 560 KB and it doesn't need to fetch anything once you got it.<p>Open it in a browser and then you can edit, present, print and save. Share it via email or via Airdrop and all they need is a browser to edit, present and also do live collab on the slides. Drop it in to Claude or ChatGPT to transform existing pptx files into Bento slides. There is no cloud involved, only an encrypted blind relay to allow for shared editing. The relay doesn't see any of the data.<p>Check it out at <a href="https://bento.page/slides/" rel="nofollow">https://bento.page/slides/</a> which takes you straight to the editor.<p>Go to <a href="https://bento.page/guestbook/" rel="nofollow">https://bento.page/guestbook/</a> to try out the live guestbook to experience share editing / collab.<p>There is also a gallery with some sample decks on the website - <a href="https://bento.page/" rel="nofollow">https://bento.page/</a><p>All the code is MIT licensed and you can find it here - <a href="https://github.com/nyblnet/bento" rel="nofollow">https://github.com/nyblnet/bento</a> . I used reveal.js with several other libraries (including some homegrown ones), and Claude Code.
Show HN: Bento - An entire PowerPoint in one HTML file (edit+view+data+collab)
Over the past few months, our team has been building more and more slidedecks using web frontend technologies with coding harnesses like Claude Code, but a common complaint is to make even small edits we need to edit the code either manually or via the harness.<p>To avoid this loop, I ended up creating Bento, a single HTML file with everything you need in a slide tool including animations and shared editing. There's no install or cloud login, everything works offline. The default deck is around 560 KB and it doesn't need to fetch anything once you got it.<p>Open it in a browser and then you can edit, present, print and save. Share it via email or via Airdrop and all they need is a browser to edit, present and also do live collab on the slides. Drop it in to Claude or ChatGPT to transform existing pptx files into Bento slides. There is no cloud involved, only an encrypted blind relay to allow for shared editing. The relay doesn't see any of the data.<p>Check it out at <a href="https://bento.page/slides/" rel="nofollow">https://bento.page/slides/</a> which takes you straight to the editor.<p>Go to <a href="https://bento.page/guestbook/" rel="nofollow">https://bento.page/guestbook/</a> to try out the live guestbook to experience share editing / collab.<p>There is also a gallery with some sample decks on the website - <a href="https://bento.page/" rel="nofollow">https://bento.page/</a><p>All the code is MIT licensed and you can find it here - <a href="https://github.com/nyblnet/bento" rel="nofollow">https://github.com/nyblnet/bento</a> . I used reveal.js with several other libraries (including some homegrown ones), and Claude Code.
Show HN: Explore 6048 YC companies as an interactive galaxy
Show HN: CodeAlmanac – Karpathy-style codebase wiki from your conversations
Hey HN! This is Divit from Almanac (YC S26). We built CodeAlmanac, a wiki for your coding agents that updates as you talk to them. It is open-source, local, and free.<p>Here’s a demo: <a href="https://www.youtube.com/watch?v=XNQWV3TFBWM" rel="nofollow">https://www.youtube.com/watch?v=XNQWV3TFBWM</a><p>Your CC/Codex conversations contain a LOT of knowledge that is forgotten because it was never documented. People have their own methods of documenting their chats. We used to make Markdown files like MANUAL.md and DESIGN.md, and would prompt Claude to keep them updated. The problem is that these files quickly become outdated and messy, and there’s only so much you can put in a single file.<p>So we set out to build CodeAlmanac. We wanted something that was 1) maintained automatically, 2) lived inside our repository, and 3) used our existing Codex/Claude Code subscriptions.<p>CodeAlmanac maintains an almanac/ folder inside your repository. It contains connected Markdown pages that cover things not documented in the codebase, including decisions you have made and why the codebase is shaped this way.<p>The pages are indexed in SQLite and are queryable through a CLI. We add instructions to AGENTS.md or CLAUDE.md so future sessions automatically search the wiki before they start coding.<p>Every five hours, CodeAlmanac uses the Codex/CC SDK to spin up an agent that reads your new conversations and updates the relevant pages. We went with a time-based trigger instead of commits because we saw people commit very frequently, which would lead to high token costs.<p>We originally made CodeAlmanac for individual developers, but the team use case has become much more obvious to us while using it ourselves. We are a team of three, and each of us works with our own coding agents. Before this, I would make some change after a lot of thinking and then later have to call my cofounders and explain why it looked this way. There is almost a sense of relief now knowing that the decisions I made are written down somewhere their agents will actually read.<p>It’s live today for everyone to try. Please let me know your feedback and I’ll be here to answer any questions. Would also love to hear how you all maintain context across conversations today!
Show HN: Imagin Raw – A 9MB Open-Source Alternative to Adobe Bridge for Mac
Show HN: Imagin Raw – A 9MB Open-Source Alternative to Adobe Bridge for Mac