The best Hacker News stories from Show from the past day
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Show HN: Audio Player with "Binaural Beats" tuned to the same key as your music
Hi HN, Gabriel here.<p>This is a standalone binaural beat generator, combined with a two-deck local audio player built with the Web Audio API.<p>It includes ten harmonic carrier pairs, seven brainwave-state presets, per-harmonic mixing (so the binaural tones aren't out-of-tune with the music), adjustable beat and carrier frequencies, two oscillator shapes, offline key/BPM analysis, automatic carrier matching, adaptive impulse noise, playlist support, and output recording.<p>The main problem I've had with existing "binaural beat" generators is that the tone generated does not remain in-tune with what I'm listening to.<p>This becomes highly noticeable, kind of ruining both the binaural beat <i>and</i> the music ("wrong, Marge, two perfectly good jackets!").<p>That's the issue this solves, and then I added a bunch of nice-to-haves on top.<p>It's open-source, free, etc - I'm just sharing because it's something I wanted to exist, and maybe others want it to exist too. Some of you might remember <i>i-Doser</i>, and have - like me - been looking for that same high ever since.<p>That (urban-legend-coded, totally-trippy-if-you're-fifteen-years-old) app introduced me to "Binaural Beats" - where you play two different tones in each of a person's ears, and the brain scrambles the different frequencies into a single frequency.<p>As I understand it, this is meant to induce specific brainwaves in the listener, which replicate states like "deep focus" or "half-asleep" or "meditating".<p>I've not decided personally if this is real science, or new-age pseudoscientific nonsense. It's probably somewhere between the two.<p>I intentionally haven't looked that hard into studies on the actual effects, because I think placebo is very powerful, and binaural beats work great on me. I find them genuinely useful for focus, meditation, and dream-state creativity. I figure: If it ain't broke, don't learn that it's a placebo. YMMV.<p>I really love ambient music, and I think that audiobooks could be a much more interesting form if they borrowed more from ideas found in ambient music. That's why I put a second deck on it for spoken-word files. There's some ASMR-y stuff like impulse noise available, also, for those good-good tingles.<p>I'd like to build it out into a more fully-featured "ambient spoken word" DJ software UI, for making stuff inspired by and adjacent to Glenn Gould's <i>The Idea of North</i>, and I guess this is currently about halfway to that thus far. The playlist function allows you to predefine the binaural tone against tracks playing in the future, so you can set up some pieces of music, and define the brain frequency you are trying to induce (or placebo) in yourself.<p>I've had a lot of fun putting albums into this, and taking journeys from - for example - "focus" to "deep sleep". I strongly recommend Sea Power's <i>Disco Elysium</i> OST for that, which I've revisited a few times in this software already.<p>I can't attest to whether the opposite directional journey works, because the "deep sleep" tone has reliably rendered me unconscious.<p>I hope some of you get some of the same enjoyment (or placebo of enjoyment) I'm getting from it. Feedback, ideas of music that might work well in it, and feature-requests appreciated, especially features in the 'this would make me enjoy using it, but I can't be bothered to add it myself' category.<p>Edit: I just realised I could have deployed this as a GH Pages page, so you can use it without any setup at all. Doh!<p>Link to that here - should all work fine in-browser:<p><a href="https://henrygabriels.github.io/binaural-studio/" rel="nofollow">https://henrygabriels.github.io/binaural-studio/</a>
Show HN: macOS menu-bar manager for SSH port forwards
is a tiny native macOS menu-bar app for local SSH port forwards. It has no external dependencies and runs natively.
Show HN: Trifle – Open-source analytics that stores answers, not events
Trifle is an open-source time-series analytics library that aggregates nested counters instead of storing raw events. All in the database you already have. After rebuilding it twice over 10 years, it now tracks ~1B events a day at my day job.<p>It started in 2015 as my own Rails APM. I plugged into ActiveSupport::Notifications, got a few small users, and one bigger one whose scraping app broke everything. That sparked the core idea: aggregate counters into pre-defined time buckets, so a single write increments multiple buckets at once. The APM eventually faded away without much traction.<p>Later in 2021 I needed analytics at my day job. Instead of going for something out there I revised the idea of Trifle as a more generic analytics library, borrowing some data warehouse ideas. First used Redis, then Postgres, eventually MongoDB. Hence why Trifle::Stats comes with multiple drivers that keep the DSL unified while storage layer changes with your needs. In our case (huge write volume, some reads) PG read faster but slowed on large writes.<p>The nested values are the whole trick here. Single:<p><pre><code> Trifle::Stats.track(
key: 'requests::aws::s3_uploads',
values: {
count: 1,
status: { request.response_code => 1 },
size: payload.bytes,
duration: { sum: request.duration, count: 1 }
}
)
</code></pre>
builds up counts for requests, success rate, result status codes, duration for multiple time buckets at once. Single bucket from 2am then looks like:<p><pre><code> { count: 14, status: { 200: 12, 500: 2 }, size: 5628341, duration: { sum: 43, count: 14 } }
</code></pre>
If request.duration is in seconds, then sum stored under duration would be in seconds as well.<p>Success rate is never stored, but it is calculated by dividing 200s over total number of requests. Same with average duration: sum over count. You ask for a metrics key, granularity and timeframe and you get back aggregated values at each point. Ready for charts or to answer "Average response time over last 30 days".<p>There's a Series wrapper for aggregating and formatting values for charts in a simple call. And as building dashboards is not as much fun for other devs as I thought, I built Trifle App - a visual layer with dashboards, scheduled digests and alerts. It's written in Elixir, so I ported the library to Elixir too. And later to Go for a CLI. All three are compatible, write in one and read in another.<p>Today we track activity from over 100M background jobs a day which turns into about 1B events. It runs surprisingly cheap when you're willing to trade some safety away (turn off journaling and write concerns in Mongo). 3-node Hetzner MongoDB cluster where the primary does 20% utilization costs us around $1k/month.<p>It has its limitations. Payloads can't hold tens of thousands of keys. Documents becomes too large to update efficiently. Some planning ahead is needed. And then there are no dimensions. Sometimes you can nest them (country - there are only so many countries), sometimes it's better to have dedicated metrics key per dimension (customer - growing forever). That multiplies tracked events, hence 1B events from 100M jobs.<p>The libraries are MIT. The App is source-available under ELv2 - free to self-host and paid cloud if you want it managed. I build this on the side with no investor money to burn on a free service.<p>Happy to answer anything about architecture, storage models, my failures or why I didn't give up on this yet.
Show HN: Whetuu – a zero-config cross-shell prompt written in Zig
Show HN: I simulated closing the Strait of Hormuz on real oil trade data
OP here: I created this visualization tool as the byproduct of a supply chain class I taught at Columbia. The pedagogical exercise grew into a full blown visualization and paper about global oil trade.<p>The model:
The mechanics are the same as the financial network Eisenberg-Noe: Instead of banks, every country consumes oil interconnected via bilateral trading. Shocks propagate throughout the network, depleting oil reserves when bottleneck nodes (such as the Strait of Hormuz) are blocked.<p>Insights:
The interesting part is the mechanics of how the crisis unfolds: for example, France receives 0 oil from Hormuz directly, yet their reserves are depleted faster because other countries reactively increase their safety oil stock, increasing oil price, making stockouts more expensive for everyone.<p>The model also gives price dynamics which are interesting on their own: the price increase is not immediate, it follows sequentially as countries reserves deplete.<p>Some caveats:
1. For producer nodes, depletion means their export slack is reduced/exhausted.
2. No sanctioned trade (UN Comtrade data)<p>Technical Details:
The visualization is 600 lines of flask plus js frontend (LLM assisted visualization with ground-truth matching the original numerical exercise of the paper)<p>Paper with proofs/theory:
<a href="https://arxiv.org/abs/2607.17491" rel="nofollow">https://arxiv.org/abs/2607.17491</a>
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: 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: Claude-thermos keeps your Claude session warm for you
Show HN: Remux – an open-source tmux workspace designed for iPhone
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: 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: 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!