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My dad cuts window profiles in his workshop and I kept watching him measure, mark, and reposition by hand hundreds of times a day. So I built him a motorized ruler that moves to any position you tap on a tablet.

It runs on an ESP32-S3 over BLE 5.3, uses hardware-timed stepper pulses (RMT peripheral, no CPU jitter) to hit 0.5mm accuracy, and pairs with a Bosch GLM laser meter - point, measure, and the ruler moves there. No typing, no transcription errors.

You can also upload cut lists from a file or email and it runs each position in sequence - perfect for batch cutting window profiles where you need the same set of measurements repeated all day.

Open hardware: off-the-shelf linear rails, NEMA 23 closed-loop stepper, inductive homing sensor. Total BOM is about 470 EUR. I sell the firmware, app (Android + desktop), and a step-by-step build guide for 350 EUR.

Video demo: https://youtu.be/GCUHBiIs4Vk

Happy to answer questions about the build or the ESP32 RMT approach to stepper control.

Comments URL: https://news.ycombinator.com/item?id=47236599

Points: 1

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I think everyone should build their own HN reader at least once in their life. This is mine and its pink. I wanted a good HN experience on mobile something I could pull up on the train and browse comfortably. So I built a PWA that works well on mobile and feels native on the phone. Along the way, I kept adding things: Semantic search – Stories are embedded with OpenAI's text-embedding-3-large and indexed in MongoDB 8.2's Vector Search. Search combines vector similarity and full-text via Reciprocal Rank Fusion, so you can find stories by meaning, not just keywords. Personalized feed: A profile vector is built from your reading history and bookmarks (weighted by time spent and recency). After a few stories, the feed adapts to your interests. Content extraction: A Playwright-based scraper fetches article text, generates thumbnails, and extracts metadata. Real-time updates and infinity scroll. Auth is passkey-first (WebAuthn) and works without personal information. Stack: Angular 21, FastAPI (Python 3.14), MongoDB 8.2, Valkey for job queues. It's a hobby project and I had a lot of fun building it. Feedback welcome!

Comments URL: https://news.ycombinator.com/item?id=47236594

Points: 1

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Article URL: https://ensue.dev/blog/beating-memory-benchmarks/

Comments URL: https://news.ycombinator.com/item?id=47236592

Points: 3

# Comments: 0

In Hybrid Search, searchers such as BM25 and Vector Search run independently, produce separate result sets, and get merged...

Introducing ... ... Wormhole Vectors!

Instead of merging orthogonal results, traverse between the vector spaces. Run a lexical search, pool the matching documents' dense vectors, and search the dense space from that point.

Hybrid search runs two queries and hopes the right answer was in one of them. Wormhole vectors let you traverse between the spaces instead.

https://www.youtube.com/watch?v=j4JZvKb5pJY

Comments URL: https://news.ycombinator.com/item?id=47236590

Points: 1

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Article URL: https://cuencahighlife.com/why-payment-fees-matter-more-than-you-think/

Comments URL: https://news.ycombinator.com/item?id=47236582

Points: 2

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Article URL: https://status.gitlab.com

Comments URL: https://news.ycombinator.com/item?id=47236570

Points: 1

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Article URL: https://www.loom.com/share/7b3ba36113e446548f3a79cf5fc1e42c

Comments URL: https://news.ycombinator.com/item?id=47235804

Points: 1

# Comments: 0

Article URL: https://mikerowe.com/2020/03/walk-me-through-this-safety-third-thing/

Comments URL: https://news.ycombinator.com/item?id=47235800

Points: 1

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Article URL: https://karozieminski.substack.com/p/perplexity-computer-review-examples-guide

Comments URL: https://news.ycombinator.com/item?id=47235767

Points: 2

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Article URL: https://www.wsj.com/business/jack-dorseys-latest-far-out-bet-an-ai-future-with-fewer-employees-25655cda

Comments URL: https://news.ycombinator.com/item?id=47235766

Points: 1

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Article URL: https://phys.org/news/2026-03-uncertainty-quantum-errors.html

Comments URL: https://news.ycombinator.com/item?id=47235764

Points: 1

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Article URL: https://www.cjroth.com/blog/2026-02-18-building-an-elite-engineering-culture

Comments URL: https://news.ycombinator.com/item?id=47235761

Points: 1

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Article URL: https://19thnews.org/2026/03/idaho-medicaid-budget-cuts-disability-programs/

Comments URL: https://news.ycombinator.com/item?id=47235746

Points: 1

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Article URL: https://e2b.dev/blog/yc-companies-ai-agents

Comments URL: https://news.ycombinator.com/item?id=47235695

Points: 1

# Comments: 2

Hi HN, We built AgentCost to solve a problem we kept running into: nobody knows what their AI agents actually cost. One line wraps your OpenAI/Anthropic client: from agentcost.sdk import trace client = trace(OpenAI(), project="my-app") From there you get a dashboard with cost forecasting, model optimization recommendations, and pre-call cost estimation across 42 models. What's included (MIT):

Python + TypeScript SDKs Real-time dashboard with 6 views Cost forecasting (linear, EMA, ensemble) Optimizer: "switch these calls from GPT-4 to GPT-4-mini, save $X/day" Prompt cost estimator for 42 models LangChain/CrewAI/AutoGen/LlamaIndex integrations Plugin system OTel + Prometheus exporters CLI with model benchmarking

Enterprise features (BSL 1.1): SSO, budgets, policies, approvals, notifications, anomaly detection, AI gateway proxy. Tech stack: Python/FastAPI, SQLite (community) or PostgreSQL (enterprise), React dashboard, TypeScript SDK. GitHub: https://github.com/agentcostin/agentcost Docs: https://docs.agentcost.in pip install agentcostin Would love feedback from anyone managing AI costs at scale.

Comments URL: https://news.ycombinator.com/item?id=47235683

Points: 2

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Article URL: https://www.deveillance.com/

Comments URL: https://news.ycombinator.com/item?id=47235673

Points: 1

# Comments: 1

Article URL: https://www.videoevaluator.com/

Comments URL: https://news.ycombinator.com/item?id=47235667

Points: 1

# Comments: 0

I built whycantwehaveanagentforthis.com — submit any problem and get a structured analysis of whether an AI agent could solve it. The output includes a creative agent name, feasibility verdict, real competitor analysis (actual products with URLs), a kill prediction (which big tech company makes this obsolete, when), build estimate, and a savage one-liner. Built with Next.js + Claude API (Haiku). Runs on ~$5/day. Rate limited with Upstash Redis (7 layers). The prompt engineering to get accurate, non-hallucinated competitor analysis was the hardest part. Free, no signup. Feedback welcome — especially on AI response quality.

Comments URL: https://news.ycombinator.com/item?id=47235650

Points: 1

# Comments: 0

Article URL: https://www.thedrive.com/news/toyota-once-used-a-fake-dining-room-set-to-teach-executives-how-big-americans-are

Comments URL: https://news.ycombinator.com/item?id=47235642

Points: 2

# Comments: 0

Background: I've been working on agentic guardrails because agents act in expensive/terrible ways and something needs to be able to say "Maybe don't do that" to the agents, but guardrails are almost impossible to enforce with the current way things are built.

Context: We keep running into so many problems/limitations today with MCP. It was created so that agents have context on how to act in the world, it wasn't designed to become THE standard rails for agentic behavior. We keep tacking things on to it trying to improve it, but it needs to die a SOAP death so REST can rise in it's place. We need a standard protocol for whenever an agent is taking action. Anywhere.

I'm almost certainly the wrong person to design this, but I'm seeing more and more people tack things on to MCP rather than fix the underlying issues. The fastest way to get a good answer is to submit a bad one on the internet. So here I am. I think we need a new protocol. Whether it's AAP or something else, I submit my best effort.

Please rip it apart, lets make something better.

Comments URL: https://news.ycombinator.com/item?id=47235632

Points: 2

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