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Show HN: Continuum – Financial simulations for DIY multimillionaires Hey HN, I&#x27;m Charles, the solo founder of Continuum (<a href="https:&#x2F;&#x2F;continuum-app.xyz&#x2F;" rel="nofollow">https:&#x2F;&#x2F;continuum-app.xyz&#x2F;</a>).<p>Continuum is a financial planning web app for people with 1-15M. It helps you create simulations of job offers, house purchases, and life events. Its goal is to replace human advisors that cost thousands, or even worse, take AUM fees of 1%.<p>The MVP includes AI voice onboarding, scenario and mural creation, and 4 modules. The simulation math engine is code to ensure determinism. The LLMs don&#x27;t do any simulation math.<p>Demo: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;EDUK26Rs0cE?si=hCSdgSDkgKJt3Ni2" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;EDUK26Rs0cE?si=hCSdgSDkgKJt3Ni2</a><p>The biggest technical challenge has been tuning the mural generation not to hallucinate. Lot&#x27;s of weird bugs!<p>The most fun part has been playing with the founder equity offers. I&#x27;ve modeled paper, expected value, and median exits (0).<p>Me: Senior software engineer @Google with 10 YOE. 5 YOE at startups before joining Google. Excited to be building fast again!<p>Looking for 10 design partners to help shape the product + roadmap. I&#x27;ll be happy to give you a demo and onboard you. 50% off the first year + VIP support.<p>No account required for the free version. There is also a 14 day Pro trial. I will give as many demos and onboard as many users as I can.<p>Reach out to charles@continuum-app.xyz

TSMC Uses Old Fabs to Make New Chips [video]

Nvidia dramatically reduces amount of OpenAI infra financing it may guarantee

Stripe Clinches over $7B Deal to Buy AI Firm OpenRouter

MathCode, Mathematical Coding Agent

Show HN: Flynt.js – 2.2kb, zero-build, CSP-safe reactivity library for MPAs

Show HN: I shrank DeepSeek V4 Flash to 57GB and it wrote a compiler on my Mac I built a specialized package of DeepSeek V4 Flash 0731 (originally 284B total parameters, 13B active), preserving reasoning, tool calling and coding capabilities:<p><a href="https:&#x2F;&#x2F;huggingface.co&#x2F;steadfastgaze&#x2F;DeepSeek-V4-Flash-0731-Coder-56.8GB-MoEspressoV2" rel="nofollow">https:&#x2F;&#x2F;huggingface.co&#x2F;steadfastgaze&#x2F;DeepSeek-V4-Flash-0731-...</a><p>I let it write a minimal C compiler targeting ARM64, then test the result with Fibonacci and FizzBuzz programs, and it succeeded in less than 1 hour, with the full recording at:<p><a href="https:&#x2F;&#x2F;youtu.be&#x2F;XiwSilmV8B0" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;XiwSilmV8B0</a><p>You can run it on Silicon Macs with my engine <a href="https:&#x2F;&#x2F;github.com&#x2F;steadfastgaze&#x2F;MoEspresso" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;steadfastgaze&#x2F;MoEspresso</a>, while one of the core libraries developed to obtain this result is available at <a href="https:&#x2F;&#x2F;github.com&#x2F;steadfastgaze&#x2F;mlx-iqk" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;steadfastgaze&#x2F;mlx-iqk</a>.<p>The above recording was on a 128GB memory MacBook M3 Max, but you can also run it on 32GB MacBooks with a very usable context (128K tokens) and projected 5 tok&#x2F;s. I did try it on a fanless 16GB memory MacBook Air M1 (1.39 tok&#x2F;s), but unfortunately the available context was very small.<p>How: - First, efficient quantisation: mlx-iqk takes advantage of IQ_K tensor encoding, more efficient than the ones available via llama.cpp or barebones MLX, originally designed by Iwan Kawrakow - I also changed the layout to a k-contiguous one, to make it faster, at least in this Metal setup.<p>- Second, expert pruning: each of the 40 learned-router layers had 256 experts, and not all of them are equally important for the coding use cases. I removed 80B parameters - this is a known technique called REAP, shared at <a href="https:&#x2F;&#x2F;www.cerebras.ai&#x2F;blog&#x2F;reap" rel="nofollow">https:&#x2F;&#x2F;www.cerebras.ai&#x2F;blog&#x2F;reap</a>.<p>- Third: balancing the cheapest IQ1_S_R4 tensor encoding (~1.5 bits per weight), selectively promoting projections to IQ2_KS or IQ2_K where the measured error reduction justified the bytes.<p>One of the main ideas was not only to save relevant knowledge, but also to not make it forget how to... stop thinking, how to use reasoning. In the first experiments, it would sometimes reason for thousands of tokens without closing its thinking section, or it would go in loops.<p>Then I solved this by heavily weighting tool-calling traces and structured reasoning in the calibration mix.

NIH is ending a key grant for budding clinical researchers

Show HN: A punch clock to help with hourly household workers My wife &amp; I have a housekeeper who works flexible days and hours during the week. She has other commitments, so she comes to our house when it works best for her, within “agreed reasonable hours” (she doesn’t come on the weekends, or before 8am).<p>Both of us are usually working in our home offices when she shows up, so I needed a way to track her hours so I could pay her every 2 weeks. I was afraid of losing a piece of paper (especially to our dog as he is fond of those), so I started keeping track of her hours in a spreadsheet. At the end of each day, I’d scrub the driveway video from our security system to pick up the timestamps. Then for payday I’d add the hours, multiply by 60, add minutes, divide by 60, and multiply by her hourly rate. Then mark it paid in the spreadsheet.<p>That worked OK, but it was a bit of a pain to use. There would be days when I would forget this process and the source-of-truth video would have aged out of the security system history. Then doing the base 60 math required a few re-verify steps as I didn’t want to risk a mistake that would be in our favor financially. As a precaution I’d always round the timestamps up&#x2F;down in her favor (which ends up being real money over a month).<p>So I grabbed Claude (my first serious AI use) and built something to simplify my life: a small web app (Spring Boot + React on AWS), with an old iPad by the door as a kiosk. She punches in and out on it, and I have an “admin” view that lets me see the time logs and compute pay automatically. It grew some legs because there&#x27;s a few extra features I realized I needed after weeks of using it (like notifications when she punches in or out, or the ability to leave her a message when I&#x27;m stuck in a meeting).<p>This was a fun side project, and since it was already on AWS I figured I could make it public and the extra cost wouldn&#x27;t be too much to bear. It actually took a lot of effort to do that (more than I wanted: email deliverability alone was a saga), but it&#x27;s there now, and it&#x27;s free.<p>Happy to answer anything about the stack, the hosting, or whatever. I don’t have Android devices to test, so I’m not sure how well it works in that world (any feedback appreciated).

St Lucie Nuclear Reactor Unit 1 manually shutdown, 3 control rods drop into core

Show HN: PageSieve, a web scraping browser extension PageSieve[1] is a browser extension for scraping data from different websites from within your browser. Currently it&#x27;s Firefox only but I am open to porting it to other browsers if that&#x27;s something people want.<p>My original motivation for this started with a desire to improve the capabilities of the selectorgadget[2] bookmarklet to support extracting as well as finding selectors.<p>The ideal use case is when some data you need is useful but you don&#x27;t want to spend the effort of writing a one-off script to extract it especially nowadays when a lot of websites need an entire JavaScript environment to load correctly which complicates scraping.<p>Using this extension means you only need to maintain the selectors for extracting the data and don&#x27;t need to worry about managing dependencies. As long as the website can be opened in your browser you should be able to scrape data from it using the extension.<p>I have a lot of features planned as well as some rough edges that need smoothing. I use this extension nearly everyday and I hope it can make somebody else&#x27;s work easier.<p>Take a look and let me know what you think.<p>[1]: <a href="https:&#x2F;&#x2F;julius383.github.io&#x2F;PageSieve&#x2F;" rel="nofollow">https:&#x2F;&#x2F;julius383.github.io&#x2F;PageSieve&#x2F;</a><p>[2]: <a href="https:&#x2F;&#x2F;github.com&#x2F;cantino&#x2F;selectorgadget&#x2F;" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;cantino&#x2F;selectorgadget&#x2F;</a>

Solution for long term memory for agent coding CLIs and to facilitate handoff between different agent vendors

AI Coding Without the Vibes

Show HN: Grafana agent observability for Hermes Agent

Show HN: PyScrappy, self-healing web scraping selectors plus an MCP server

Zapping Rocks Unlocks Stimulated Geologic Hydrogen

Show HN: Widen, a native Postgres GUI using Apple's on-device LLM

Show HN: I built a native app for coding agents with Rust and GPUI

Decoding smell: Study reveals how odor signals shapeshift in the wind

AI-Assisted GPU Porting of a 250k Line Legacy Weather Simulation Code