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Hey HN, we trained and open-sourced a 1.5B model that predicts your next edits, similar to Cursor. You can download the weights here (https://huggingface.co/sweepai/sweep-next-edit-1.5b) or try it in our JetBrains plugin (https://plugins.jetbrains.com/plugin/26860-sweep-ai-autocomp...).

Next-edit autocomplete differs from standard autocomplete by using your recent edits as context when predicting completions. The model is small enough to run locally while outperforming models 4x its size on both speed and accuracy.

We tested against Mercury (Inception), Zeta (Zed), and Instinct (Continue) across five benchmarks: next-edit above/below cursor, tab-to-jump for distant changes, standard FIM, and noisiness. We found exact-match accuracy correlates best with real usability because code is fairly precise and the solution space is small.

Prompt format turned out to matter more than we expected. We ran a genetic algorithm over 30+ diff formats and found simple `original`/`updated` blocks beat unified diffs. The verbose format is just easier for smaller models to understand.

Training was SFT on ~100k examples from permissively-licensed repos (4hrs on 8xH100), then RL for 2000 steps with tree-sitter parse checking and size regularization. The RL step fixes edge cases SFT can’t like, generating code that doesn’t parse or overly verbose outputs.

We're open-sourcing the weights so the community can build fast, privacy-preserving autocomplete for any editor. If you're building for VSCode, Neovim, or something else, we'd love to see what you make with it!

Creator here. I built ChartGPU because I kept hitting the same wall: charting libraries that claim to be "fast" but choke past 100K data points.

The core insight: Canvas2D is fundamentally CPU-bound. Even WebGL chart libraries still do most computation on the CPU. So I moved everything to the GPU via WebGPU:

- LTTB downsampling runs as a compute shader - Hit-testing for tooltips/hover is GPU-accelerated - Rendering uses instanced draws (one draw call per series)

The result: 1M points at 60fps with smooth zoom/pan.

Live demo: https://chartgpu.github.io/ChartGPU/examples/million-points/

Currently supports line, area, bar, scatter, pie, and candlestick charts. MIT licensed, available on npm: `npm install chartgpu`

Happy to answer questions about WebGPU internals or architecture decisions.

I saw a tweet that tinygrad is so good that you could make a graphics library that wraps tg. So I’ve been hacking on a gtinygrad, and honestly it convinced me it could be used for legit research.

The JIT + tensor model ends up being a really nice way to express light transport all in simple python, so I reimplemented some new research papers from SIGGRAPH like REstir PG and SZ and it just works. instead of complicated cpp its just a 200 LOC of python.

RailsUI is a comprehensive open-source library of UI components and design tools for building modern, responsive web applications with Ruby on Rails. It provides a range of pre-built, visually appealing components that can be easily integrated into Rails projects to accelerate development and enhance the user experience.

The article describes a Hartree-Fock solver, a computational tool used to solve the Hartree-Fock equations for electronic structure calculations. The solver is implemented in Python and can be used to study the electronic properties of atoms and molecules.

I built this as a small side project to learn and experiment, and I ended up with this!

I used a subdomain from my personal portfolio, and everything else runs on free tiers.

The project uses Nuxt, SVG, Cloudflare Workers, D1 (SQL), KV, Terraform, and some agentic coding with OpenAI Codex and Claude Code.

What started as a joke among friends turned into a fun excuse to build something end to end, from zero to production, and to explore a few things I’d never touched before.

I’d really appreciate any feedback or suggestions.

I made this for myself, and it seemed like it might be useful to others. I'd love some feedback, both on the threat model and the tool itself. I hope you find it useful!

Backstory: I've been using many agents in parallel as I work on a somewhat ambitious financial analysis tool. I was juggling agents working on epics for the linear solver, the persistence layer, the front-end, and planning for the second-generation solver. I was losing my mind playing whack-a-mole with the permission prompts. YOLO mode felt so tempting. And yet.

Then it occurred to me: what if YOLO mode isn't so bad? Decision fatigue is a thing. If I could cap the blast radius of a confused agent, maybe I could just review once. Wouldn't that be safer?

So that day, while my kids were taking a nap, I decided to see if I could put YOLO-mode Claude inside a sandbox that blocks exfiltration and regulates git access. The result is yolo-cage.

Also: the AI wrote its own containment system from inside the system's own prototype. Which is either very aligned or very meta, depending on how you look at it.

Hi HN,

I built this because I often work in coworking spaces or do screen sharing, and I've always had this fear of accidentally flashing my .env file with production secrets to the whole room (or recording).

It’s a simple VS Code extension that opens .env files in a custom grid editor. It automatically masks any value longer than 6 characters so I can safely open the file to check keys without exposing the actual secrets.

It runs 100% locally with zero dependencies (I know how sensitive these files are). It just reads the file, renders the grid, and saves it back as standard text.

It's open source (MIT) and I'd love any feedback on the masking logic or other features that would make it safer to use.

Marketplace: https://marketplace.visualstudio.com/items?itemName=xinbenlv... Github https://github.com/xinbenlv/dotenv-mask-editor

The article discusses Better Icons, an open-source library that provides a collection of high-quality, customizable icons for web and mobile applications. It highlights the library's features, such as easy integration, extensive icon set, and support for various frameworks and platforms.

Hey HN! I built Retain as the evolution of claude-reflect (github.com/BayramAnnakov/claude-reflect).

The original problem: I use Claude Code/Codex daily for coding, plus claude.ai and ChatGPT occasionally. Every conversation contains decisions, corrections, and patterns I forget existed weeks later. I kept re-explaining the same preferences.

claude-reflect was a CLI tool that extracted learnings from Claude Code sessions. Retain takes this further with a native macOS app that:

- Aggregates conversations from Claude Code, claude.ai, ChatGPT, and Codex CLI - Instant full-text search across thousands of conversations (SQLite + FTS5)

It's local-first - all data stays in a local SQLite database. No servers, no telemetry. Web sync uses your browser cookies to fetch conversations directly.

Yashiki is a simple and lightweight blogging platform built with Ruby on Rails. It offers a minimalistic design, easy-to-use content management, and the ability to host multiple blogs on a single domain.

Hey folks, I’m Hassan, one of the co-founders of Infracost (https://www.infracost.io). Infracost helps engineers see and reduce the cloud cost of each infrastructure change before they merge their code. The way Infracost works is we gather pricing data from Amazon Web Services, Microsoft Azure and Google Cloud. What we call a ‘Pricing Service’, which now holds around 9 million live price points (!!). Then we map these prices to infrastructure code. Once the mapping is done, it enables us to show the cost impact of a code change before it is merged, directly in GitHub, GitLab etc. Kind of like a checkout-screen for cloud infrastructure.

We’ve been building since 2020 (we were part of YC W21 batch), and iterating on the product, building out a team etc. However, back in 2020 one of our users asked if we can also show the carbon impact alongside costs.

It has been itching my brain since then. The biggest challenge has always been the carbon data. The mapping of carbon data to infrastructure is time consuming, but it is possible since we’ve done it with cloud costs. But we need the raw carbon data first. The discussions that have happened in the last few years finally led me to a company called Greenpixie in the UK. A few of our existing customers were using them already, so I immediately connected with the founder, John.

Greenpixie said they have the data (AHA!!) And their data is verified (ISO-14064 & aligned with the Greenhouse Gas Protocol). As soon as I talked to a few of their customers, I asked my team to see if we can actually finally do this, and build it.

My thinking is this: some engineers will care, and some will not (or maybe some will love it and some will hate it!). For those who care, cost and carbon are actually linked; meaning if you reduce the carbon, you usually reduce the cost of the cloud too. It can act as another motivation factor.

And now, it is here, and I’d love your feedback. Try it out by going to https://dashboard.infracost.io/, create an account, set up with the GitHub app or GitLab app, and send a pull request with Terraform changes (you can use our example terraform file). It will then show you the cost impact alongside the carbon impact, and how you can optimize it.

I’d especially love to hear your feedback on if you think carbon is a big driver for engineers within your teams, or if carbon is a big driver for your company (i.e. is there anything top-down about carbon).

AMA - I’ll be monitoring the thread :)

Thanks

Hi HN, we're Sam, Shane, and Abhi.

Almost a year ago, we first shared Mastra here (https://news.ycombinator.com/item?id=43103073). It’s kind of fun looking back since we were only a few months into building at the time. The HN community gave a lot of enthusiasm and some helpful feedback.

Today, we released Mastra 1.0 in stable, so we wanted to come back and talk about what’s changed.

If you’re new to Mastra, it's an open-source TypeScript agent framework that also lets you create multi-agent workflows, run evals, inspect in a local studio, and emit observability.

Since our last post, Mastra has grown to over 300k weekly npm downloads and 19.4k GitHub stars. It’s now Apache 2.0 licensed and runs in prod at companies like Replit, PayPal, and Sanity.

Agent development is changing quickly, so we’ve added a lot since February:

- Native model routing: You can access 600+ models from 40+ providers by specifying a model string (e.g., `openai/gpt-5.2-codex`) with TS autocomplete and fallbacks.

- Guardrails: Low-latency input and output processors for prompt injection detection, PII redaction, and content moderation. The tricky thing here was the low-latency part.

- Scorers: An async eval primitive for grading agent outputs. Users were asking how they should do evals. We wanted to make it easy to attach to Mastra agents, runnable in Mastra studio, and save results in Mastra storage.

- Plus a few other features like AI tracing (per-call costing for Langfuse, Braintrust, etc), memory processors, a `.network()` method that turns any agent into a routing agent, and server adapters to integrate Mastra within an existing Express/Hono server.

(That last one took a bit of time, we went down the ESM/CJS bundling rabbithole, ran into lots of monorepo issues, and ultimately opted for a more explicit approach.)

Anyway, we'd love for you to try Mastra out and let us know what you think. You can get started with `npm create mastra@latest`.

We'll be around and happy to answer any questions!

The article discusses the Texas power grid operator ERCOT, its role in managing the state's electricity supply, and the challenges it has faced during extreme weather events that have led to widespread power outages. It examines the grid's vulnerabilities and the ongoing debate around its regulation and reform.

I been building a view into companies based on their job postings. I am hoping it is useful to get a quick snapshot on the company say for interview prep etc. Please be advised that it is still a WIP in terms of data quality. Example urls: https://jobswithgpt.com/company-profiles/openai/ https://jobswithgpt.com/company-profiles/advanced-micro-devi...

Hi HN! I built Ghibli Search, a semantic search engine for Studio Ghibli movie scenes (e.g. Spirited Away, My Neighbor Totoro, Howl's Moving Castle, etc.).

Describe a dreamscape like "flying through clouds at sunset" or upload an image, and it finds visually similar scenes from the films.

Live demo: https://ghibli-search.anini.workers.dev/

Full Cloudflare stack: Workers, AI Search, R2, Workers AI

Open source: https://github.com/aninibread/ghibli-search

Would love feedback on the search quality and any ideas for improvements!

My friends and I love putting stickers on our laptops, but most printing companies have a minimum order of a single design. I have a little hobby where I print and cut multiple single designs in one order at home and get great results. Perfect for folks who want to decorate and personalise their laptops!

You can get a dozen or so stickers for just AUD$20 (approx USD$14, €12, £10) and free worldwide shipping.

Hi Hacker News! I’m David Huie. I’m launching SpeechOS, a drop-in voice input SDK for web apps.

I was inspired by Wispr Flow and wanted the same workflow inside business apps (CRMs, docs, forms, support tools), not just a standalone tool. It’s saved me a massive amount of time vs typing.

How it works: add a couple lines of JS plus an API key, and SpeechOS shows a small mic widget on every text field.

Live demo: https://www.speechos.ai/

(Click a text box and the mic widget appears; click the gear icon to see settings, custom vocabulary, and snippet configuration.)

Users can:

- Dictate: speak naturally, real-time voice to polished text (punctuation, no filler/typos)

- Edit: say “make it shorter”, “fix grammar”, “translate...”

- Command: define Siri-style app actions (for example, “submit form”, “mark complete”), and we match intent to your commands

It also supports:

- Custom vocabulary: domain terms and names (product names, acronyms, jargon) so they transcribe correctly

- Text snippets: saved reusable blocks of text you can insert by voice (for example, “my signature”, “disclaimer”, “my address”)

Why: text entry speed and accuracy still matter for productivity tools. A large-scale text entry dataset with 37,370 participants showed an average typing speed of 36.2 WPM and ~2.3% uncorrected errors. In speech research, speech recognition was about 3× faster than keyboard input and had ~20.4% lower error rate for English text entry. (https://hci.stanford.edu/research/speech/)

SpeechOS is currently in beta and free for now. Sign up at https://app.speechos.ai/accounts/signup/ and enter this HN-only beta code: HN-JFc74cVC (please don’t share outside HN).

Links:

SDK repo: https://github.com/speechos-org/speechos-client

Demo: https://www.speechos.ai/

Signup (code: HN-JFc74cVC): https://app.speechos.ai/accounts/signup/

I’d love feedback on:

1) Where this is most valuable in your stack (notes? docs? CRM entry? support macros?)

2) What you’d want from voice commands/snippets configuration

3) What would make you comfortable shipping this (privacy/security, latency, pricing)

If you’re building anything in voice AI/dictation, I’d be happy to swap notes (david@speechos.ai).

Hi HN, I’m experimenting with a small Python library called PicoFlow for building LLM agent workflows using a lightweight DSL.

I’ve been using tools like LangChain and CrewAI, and wanted to explore a simpler, more function-oriented way to compose agent logic, closer to normal Python control flow and async functions.

PicoFlow focuses on: - composing async functions with operators - minimal core and few concepts to learn - explicit data flow through a shared context - easy embedding into existing services

A typical flow looks like:

  flow = plan >> retrieve >> answer
  await flow(ctx)

This is still early and very much a learning project. I’d really appreciate any feedback on the DSL design, missing primitives, or whether this style feels useful for real agent workloads.

Repo: https://github.com/the-picoflow/picoflow

skills.sh is a website that provides a comprehensive platform for individuals to learn and enhance their skills in various domains, including programming, design, and project management. The site offers a wide range of courses, tutorials, and resources to help users develop their knowledge and expertise.

I built this because I got tired of Stockfish giving me evaluations (+0.5) without explaining the actual plan. Most opening explorers focus on statistics (Win/Loss/Draw). I wanted a tool that explains the strategic intent behind the moves (e.g., "White plays c4 to clamp down on d5" vs just "White plays c4"). The Project: Comprehensive Database: I’ve mapped and annotated over 3,500 named opening variations. It covers everything from main lines (Ruy Lopez, Sicilian) to deep sidelines. Strategic Visualization: The UI highlights key squares and draws arrows based on the textual explanation, linking the logic to the board state dynamically. Hybrid Architecture: For the 3,500+ core lines, it serves my proprietary strategic data. For anything deeper/rarer, it seamlessly falls back to the Lichess Master API so the explorer remains functional 20 moves deep. Stack: Next.js (App Router), MongoDB Atlas for the graph data, and Arcjet for security/rate-limiting. It is currently in Beta. I am working on expanding the annotated coverage, but the main theoretical landscape is mapped. Feedback on the UI/UX or the data structure is welcome.

Hey HN! I built TopicRadar to solve a problem I had with staying on top of what's trending in AI/ML without checking 7+ sites daily.

https://apify.com/mick-johnson/topic-radar

What it does: - Aggregates from HackerNews, GitHub, arXiv, StackOverflow, Lobste.rs, Papers with Code, and Semantic Scholar - One-click presets: "Trending: AI & ML", "Trending: Startups", "Trending: Developer Tools" - Or track custom topics (e.g., "rust async", "transformer models") - Gets 150-175 results in under 5 minutes

Built for the Apify $1M Challenge. It's free to try – just hit "Try for free" and use the default "AI & ML" preset.

Would love feedback on what sources to add next or features you'd find useful!

Hi HN,

I’m Eric, and I built www.meetgoran.com

Goran is a Mac/Windows desktop app for sales teams that turns your calls into actionable coaching: what was covered (and what wasn’t), what to improve next time, and patterns across reps.

First 5 meetings free (no credit card) ~ 10 min setup.

Connect Google Calendar so it can join scheduled meetings and capture the call context

What it does (practical, not “AI magic”):

- Produces structured sales notes + highlights key moments (pricing pushback, next steps, etc.)

- Scores calls against frameworks like MEDDIC/BANT (or your own checklist)

- Helps managers compare “top vs median” behaviors without rewatching hours of recordings

- Makes good examples searchable so new reps can copy what’s already working

Why I built it:

In most teams, the best talk-tracks stay trapped in recordings nobody rewatches, and managers don’t have time to coach every rep. I wanted something that turns calls into a “self-building playbook” reps will actually use.

What I’d love feedback on:

- Where would this not fit (team size, workflow, compliance constraints)?

- What should be the “default” output: rep coaching, manager dashboard, or team playbook?

Super happy with all responses.

This article discusses the challenges and strategies involved in building a successful online business, including the importance of identifying a profitable niche, providing valuable content, and leveraging various marketing channels to attract and retain customers.

The ocrbase project is an open-source optical character recognition (OCR) engine that provides a modular and extensible framework for developing and deploying OCR solutions. It aims to offer a flexible and customizable platform for performing text extraction from various types of documents.

The article discusses a Unicode detector tool that can identify the character encoding of text, allowing users to detect and troubleshoot encoding issues. The tool supports various encoding formats and provides a user-friendly interface for testing and analyzing text.

Google reviews piss me off because it's either 1 star or 5 stars and who knows if the person actually ate the food. I wish people had to visit a place multiple times, spaced out, before their ratings would even count. I decided to take that philosophy and apply it to one dish, Phở.

I'm going to all the restaurants that serve phở in Portland, Oregon's city limits and rating them. You can see the ranking, but not the point values until I've visited twice. Technically you can go get the data as it's open source and get the scores, but most people are too lazy to do that. It's a simple static site built with astro, so hammer away at what I did wrong.

I've been going in spurts over the past couple years and I'm hoping to get through the first round within the next month or two. I'm a white guy that doesn't know anything so of course the ratings are completely subjective.

I spent 15 years in FP&A and corporate budgeting. For the first 10, I actively avoided management because the path to leadership seemed designed for extroverts. Be more visible. Speak up in meetings. Network at happy hours.

I eventually got promoted anyway, mostly by taking ownership of problems I cared about fixing more than I cared about avoiding meetings. Turns out if you run the meeting, you can keep it organized.

Recently I taught myself to code using AI tools (Cursor + Claude, mostly) and wanted to build something that would have helped my past self. The result is leadquiet.com an AI coaching tool specifically for introverts in leadership roles.

It's text-based (no video calls), focused on energy management and leveraging introvert strengths rather than "overcoming" them, and priced for individuals ($15/mo).

I'm not from tech. I still work full-time in finance. Built this nights and weekends. Would love feedback if you try it.