Каталог продуктов
Отслеживается продуктов: 468
An AI agent that trades inside limits you set, starting on paper
Show HN: An AI agent that trades inside limits you set, starting on paper
ExANS
Hi HN, We are the developers of OpenLake, an open source storage engine for KV cache offloading to remote disk and memory. Once we offloaded to local disk, we realized the bottleneck is the PCIe or NIC bandwidth. We wondered whether on GPU lossless compression is viable for fast reads and lower TTFT. BF16 is usually very hard to compress, (high entropy of sign/mantissa). What surprised us is that real world KV blocks are very different. The exponent byte has a very low entropy and barely populated. Instead of compressing the whole tensor, we compress only the exponent stream on the GPU. We see the following results: (H100, production KV snapshot): - 1.51× lossless compression - 622 GB/s median GPU decode Decompression is ~10× faster than a 400 Gb/s NIC bandwidth delivering data losslessly without quality change. We've are open sourcing this as: ExANS which will be available through our vLLM and SGLang connectors on OpenLake v0.8 version. No changes are required in the inference engine. I'm curious how others are handling KV transfer today. Are you using KV compression or is bandwidth not a bottleneck yet? Thanks! GitHub: https://github.com/openlake-project/openlake Technical Blog: https://theopenlake.com/blog/exans-lossless-gpu-compression-...
Vocab Top
Show HN: Vocab Top – AI-powered vocabulary builder that helps you retain words
What if one fund held tokenized Gold, tech stocks and digital assets?
That's the idea behind Wealtii. I built a platform that lets anyone invest from just $10 into diversified funds spanning tokenized precious metals, tokenized equities, and leading digital assets. Everything is fully automated, so investors can gain diversified exposure without manually managing multiple assets or platforms. We recently launched our new RWA Blended Funds, and to celebrate the launch, all platform fees are 0% for a limited time. I'd love to hear what the HN community thinks about the concept, the product, and where you think it could be improved.
Systematic reporting in under an hour, not months
Show HN: Systematic reporting in under an hour, not months
I made a private self-destructing image hosting site in Golang
So I'm a junior Go dev and I made a small site that allows me to share images with someone privately and securely, and give me control over how many times the private link can be shared and how long the image will be accessible for. There are similar services to this one, but they are either convoluted with ads, or require sign-up, or expect you to fill out a survey or something. picburn is small, safe, and has no interest (nor the code or the server space) for storing your uploads. give it a try if it fits your usecase and let me know what you think. Note: there was a typo in the url
LarpIn
Show HN: LarpIn – LinkedIn, but everyone admits they're larping, one global feed
Mstat a temperature and stat tracker for macOS in Zig and SwiftUI
Turns out it's weirdly hard to quickly check cpu temp on M series macs so whipped this up, also has cpu usage, memory, and lets you show multiple or just an icon for the popover.
FutureSearch, AI forecasting you can verify
*Title:* Show HN: FutureSearch, AI forecasting you can verify AI forecasting is now approximately superhuman. Today, FutureSearch is exiting our long public beta and launching. We started FutureSearch in August 2023. (We’re the original AI forecasting company, at least in a Tetlock-ian, “forecast anything” sense.) We’re currently #1 of 194 in the most competitive AI forecasting tournament [1], and we score above the #3 and #2 human forecasters in the premier mixed human-bot tournaments [2]. Many people on HN seem to equate forecasting with prediction markets and finance. FutureSearch is not a financial tool, in the same way that “deep research” is not a financial tool. Yes, we do evaluate our forecaster on prediction markets [3]. But forecasting is about being as accurate about the future as possible, and the real game is in forecasting scientific progress, geopolitics, and the future of humanity. Our founding team came from Metaculus, where we pushed human forecasting to the limit on questions like when AGI would arrive. At. FutureSearch, we co-authored the AI 2027 timeline forecast, where we predicted superhuman coding and research would come around 2032, longer than the other authors, but still shorter than skeptics [4]. Thousands of people used the FutureSearch beta and ran >10k high-effort forecasts, on all sorts of diverse topics. Ask it anything about the future. We now support decision forecasts too: “If I do X, will I achieve this outcome?” Forecasting, as a capability, is useful even at the level of expert human crowds. But we predict that we will soon have strongly superhuman forecasting. People who bet against AI capability trend lines tend to lose, and the trend line in AI forecast accuracy tells a pretty clear story [5]. There’s no reason to think the best human forecasters have figured out everything predictable about the world. There’s a lot more signal to be found. And if you’re skeptical, try it. We’ve seen our fair share of exaggerated claims about AI forecasting accuracy [6]. So part of the reason we made the free tier give a few of our highest effort forecasters free is to let anyone verify the quality. [1] https://www.metaculus.com/tournament/summer-futureeval-2026/ [2] evals.futuresearch.ai [3] markets.futuresearch.ai [4] https://ai-2027.com/research/timelines-forecast [5] https://www.astralcodexten.com/p/the-ai-superforecasters-are... [6] https://www.lesswrong.com/posts/uGkRcHqatmPkvpGLq/contra-pap...
Product analytics (and evals) for agent sessions on your MCP
Hi HN! We’re Theodore and Louis, founders of Armature (YC P26). We reconstruct the entire session behind the MCP tool calls you receive, including what the user asked their agent to do and what the agent thought. You wrap your MCP in 3 lines of code (our SDK is available in Typescript, Python and Go) and start seeing in your dashboard: - All sessions reconstructed: it’s like reading the real conversation the user had inside Claude or ChatGPT! - A ranking of your MCP most popular use cases, built from sessions clustering - The most frequent issues your users’ agents encounter so you can fix them. Here is a quick demo: https://youtu.be/ZFlvquhyNMQ The story behind this is that we initially launched Armature as a standalone testing tool (https://www.ycombinator.com/launches/QQc-armature-making-you...) that could naturally be used through an MCP itself. We quickly realized we had no idea how our users were using Armature MCP and if they were satisfied with it or frustrated. It’s something we had also experienced in our previous companies: Louis built MCPs exposed to millions of users and Theo was a Forward Deployed Engineer at Palantir before joining a Datadog spin-off as Founding Engineer. Both testing and product analytics had always been real pains when exposing a product to agents but we always thought there wasn’t much we could do about analytics because the conversation lived in our users’ AI client. Then it struck us: what if we asked the agents why they were making this or that tool call? And what’s the user's intent or potential frustration? So we started experimenting with MCP instrumentation and the use-cases actually surprised us! Many of our first customers had implemented workarounds for their CI to trigger new tests or for their coding agents to fetch the results efficiently. Even though we talked to our first users regularly, they had never shared this feedback with us. We then built automations to automatically cluster use-cases, identify issues frequently encountered and let our own coding agents fix them. When our CTO friends heard about this, they wanted to try it for themselves so we gave them access to a cloned version of our internal product and they started sharing feedback like they never did on our “real” product! That’s when we decided to start working seriously on MCP Analytics as a product. At first we were afraid of degrading MCP performance so we iterated until we reached the exact same success rate as without our instrumentation (89.17 % vs 89.15 % pass rate out of 870 runs). Then privacy was an obvious constraint so we applied the same methods we had learned from working with banking data or building sensitive data scanning in logs. Today, redaction runs client-side before reaching our servers. There are still a lot of things we haven’t fully figured out: not all fields are equally filled by all models, session fingerprinting for serverless / stateless MCPs isn’t perfect, and use-case clustering remains to be optimized. But we are finally launching our analytics product to everyone, self-serve at https://armature.tech with a set-up that takes less than 5 minutes and a generous free tier. And now we are working on fully closing the loop, bringing evals back in our product so we can: identify top workflows and issues -> recommend fixes and improvements -> test fixes at scale on the same workflows run by users, across all harnesses and models -> open PRs to ship fixes directly. The evals can be generated automatically from the session analytics so you can catch every regression and can test every improvement’s real impact across all models and harnesses before shipping it. Here’s an example to make it more concrete: 10 days ago, a marketing automation platform which has had early access to what we built for weeks identified thanks to MCP Analytics that users were frustrated not being able to change their target audience after campaign creation. So they shipped the feature and tested it successfully locally with Claude Code on Fable 5. Then a few days later when preparing their new MCP public release, they ran a suite of evals on Armature and realized that small models could hallucinate audience_ids which would lead their MCP to send the campaign to ALL their contacts by default (which could obviously lead to disasters in prod). This is the kind of story that makes what we are building feel so helpful! Now, the most useful feedback for us would be to know what’s still missing in our product so you can feel you are now in full control of the “Agent Experience”. And if you run an MCP in production we’d also love to know: what do you do today to know if agents succeed and if the users behind them are happy?
We Fixed UniFi's Slow PPPoE Performance with PPPoE Half-Bridge
Show HN: We Fixed UniFi's Slow PPPoE Performance with PPPoE Half-Bridge
Bor
Hi HN! I've been working on Bor, an open-source system for centralized Linux desktop management. Bor consists of a lightweight Go agent and a central server. Policies are streamed to clients over mTLS/gRPC in real time—no polling—and currently support Firefox, Chrome, KDE, dconf, polkit and package management, with more coming. Version 0.8 introduces several new policy types - Thunderbird, Microsoft Edge for Business and FirewallD zones, along with a number of improvements and fixes. I'd love feedback on the architecture, policy model, and whether this is something you'd consider for managing Linux workstations.