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Show HN: Epismo CLI – Make human-AI workflows reusable, like GitHub did for code

Hi HN, I'm Hiroki, founder of Epismo. Just released the Epismo CLI (https://npmjs.com/package/epismo). 380+ downloads right after launch. Thank you.The problem: I got a great result in a Claude Code thread. A week later I couldn't reproduce how I got there. The real workflow lived across chat histories, tabs, tool settings, and tiny followup prompts. Prompts copy easily, but multi-step processes don't.If GitHub made code reusable and Hugging Face made models re

AI overly affirms users asking for personal advice

<a href="https://arxiv.org/abs/2602.14270" rel="nofollow">https://arxiv.org/abs/2602.14270</a><p><a href="https://www.science.org/doi/10.1126/science.aec8352" rel="nofollow">https://www.science.org/doi/10.1126/science.aec8352</a>

Show HN: Building your first ASGI framework – step-by-step lessons

I am writing a series of lessons on building an ASGI framework from scratch. The goal is to develop a deeper understand of how frameworks like FastAPI and Starlette work.A strong motivation for doing this is because - I have been using AI to write code lately. I prompt, I get code, it works. But somewhere along the way I see I stopped caring about what is actually happening. So, this is an attempt to think beyond prompts and build deeper mental models of things we use in our day to day lives. I

Ask HN: M5 MacBook Pro buyers, worth spending the $$$ to maybe run LLMs local?

To anyone upgrading their daily driver Mac this year, are you considering going to a Max + high memory config? eg. with the hope (now or in near future) of being able to do usefully run agents/LLMs locally on your main machine?Or is the few extra thousand dollars difference between a base and max-spec MBP still just better spent on literally any other practical option (like different harware, remote hardware, cloud AI subscriptions or credits). Or wait to see if there will be an M5 Studio o

Show HN: WhatToBuy – Describe your situation, get AI-curated shopping carts

Before reading text please try the app https://www.whattobuy.app (to get great UX feedback)Shopping research is one of the most challenging tasks and people spend 30-60 min before buying an item. We developed a platform called “WhatToBuy” to save people time. In some cases shoppers are not super aware of what to really order for a trip or occasion. Our app helps them to get a range of products needed for each use-cases hence saving time and money.App workflow: Describe your situation i

Show HN: Vyasa – A client-side AI writing detector (WASM, no API calls)

Now that Wikipedia has banned AI generated articles. - https://en.wikipedia.org/wiki/Wikipedia:Writing_articles_wit...I wanted to try and see if it was possible to get somewhat decent engine based on signs of AI writing from wikipedia themselves.It runs entirely in the browser via WASM. Added instructions to further add more ways to figure out as we find out more about LLMs.Would love feedback!!, especially: - cases where it completely fails - patterns you think are stronger

Show HN: HF-agents, CLI extension to find the best model/quant for your hardware

We've been building out CLI extensions for the Hugging Face hub, and hf-agents is a fun one to share.It uses llmfit under the hood to profile your hardware and automatically select the best-fit model and quantization — no manual GGUF hunting. It then launches a Pi Agent on top of it. One command, local, fully open.If you've been using Claude Code or Codex CLI and want something that runs entirely on your own hardware/models, this is a nice lightweight alternative to try.Happy to a

Show HN: Real-time local TTS (31M params, 5.6x CPU, voice cloning, ONNX)

Hi guys and gals, I made a TTS model based on my highly upgraded VITS base, conditioned on external speaker embeddings (Resemble AI's Resemblyzer).The model, with ~31M parameters (ONNX), is tuned for latency and local inference, and comes already exported. I was trying to push the limits of what I could do with small, fast models. Runs 5.6x realtime on a server CPUIt supports voice cloning, voice blending (mix two or more speakers to make a new voice), the license is Apache 2.0 and it uses

Show HN: ClawMem – Open-source agent memory with SOTA local GPU retrieval

So I've been building ClawMem, an open-source context engine that gives AI coding agents persistent memory across sessions. It works with Claude Code (hooks + MCP) and OpenClaw (ContextEngine plugin + REST API), and both can share the same SQLite vault, so your CLI agent and your voice/chat agent build on the same memory without syncing anything.The retrieval architecture is a Frankenstein, which is pretty much always my process. I pulled the best parts from recent projects and researc

Show HN: How I built a resume editor using AI with zero web dev experience

Hi,I have recently been applying for summer internships and got frustrated when tailoring my resumes in Word. I started learning Python last autumn, but had absolutely zero experience with web development or deploying something to the front/backend. I wanted to experiment with the new coding agents to build a resume editor that would make my application process less painful.Here it is: www.tailortojob.appHow I built it: A friend helped me set up the initial infrastructure because I struggle

Launch HN: Canary (YC W26) – AI QA that understands your code

Hey HN! We're Aakash and Viswesh, and we're building Canary (https://www.runcanary.ai). We build AI agents that read your codebase, figure out what a pull request actually changed, and generate and execute tests for every affected user workflow.Aakash and I previously built AI coding tools at Windsurf, Cognition, and Google. AI tools were making every team faster at shipping, but nobody was testing real user behavior before merge. PRs got bigger, reviews still happened in fil

Show HN: Sup AI, a confidence-weighted ensemble (52.15% on Humanity's Last Exam)

Hi HN. I'm Ken, a 20-year-old Stanford CS student. I built Sup AI.I started working on this because no single AI model is right all the time, but their errors don’t strongly correlate. In other words, models often make unique mistakes relative to other models. So I run multiple models in parallel and synthesize the outputs by weighting segments based on confidence. Low entropy in the output token probability distributions correlates with accuracy. High entropy is often where hallucinations

Where do we stand with Claude 20x Max vs. Codex Pro after Opus 1M context window

Has anyone tried the latest and greatest models of both camps, with the highest thinking level and maximum possible context window setting, and compared performances and observed patterns / specific behaviors which make you choose one over the other? [Of course, everyone's mileage varies, but still want to gather insights from folks who have the privilege to be able to use both extensively]I'm talking about $200 versions of both.I couldn't find any such detail over the web fo

Show HN: Oo – compress output for coding agents (cargo test → "47 passed, 2.1s")

I've been running coding agents heavily for the past year or so using frontier model APIs, open weight model APIs and, most recently, local models (Qwen family models on a Strix Halo).Starting to run local inference has highlighted something I've been aware for longer: just running tests output shedloads of text into the context window that is there for good until compaction or starting afresh. For example, a single `cargo test` dumping 8KB into the agent's context just to communi

Show HN: AI Roundtable – Let 200 models debate your question

Hey HN! After the Car Wash Test post got quite a big discussion going (400+ comments, https://news.ycombinator.com/item?id=47128138), I spent the past few weeks building a tool so anyone can run these kinds of questions and get structured results. No signup and free to use.You type a question, define answer options, pick up to 50 models at a time from a pool of 200+, and they all answer independently under identical conditions. No system prompt, structured output, same setup for e

Using Catastrophic Forgetting as a Knowledge Topology Probe

I'm an undergrad with no research affiliation. I've been thinking about why LLM training is so expensive and why continuous learning remains unsolved. This post is where that thinking led — a concrete architecture proposal with a cheap falsifiable experiment at its core.The Core Idea (30 seconds) Catastrophic forgetting — when fine-tuning a model on new knowledge destroys old knowledge — is universally treated as a problem to minimize. I think it's a measurement instrument. The fo

Best Mesh Wi-Fi Routers for 2026

Find the best mesh Wi-Fi router system for your needs. Our team of experts evaluated mesh routers on a variety of criteria to determine the best option for any use case.

What is 1 gigabit internet? Is it worth the cost?

Gigabit internet (1 GB) has extremely fast download speeds of 1,000 Mbps. Although having the fastest internet possible might sound like a good idea, you don't want to overpay for speeds you don't ...

What to Look for in a Home Internet Plan in 2026

Choosing from the best home internet plans in 2026 is no longer just about picking the fastest option available. As technology evolves and households become more connected than ever, selecting the right home internet plan requires a deeper understanding of performance, reliability, pricing, and future readiness. With smart homes, remote work, streaming, and gaming all competing for bandwidth, your home internet needs to keep up without interruptions or hidden costs. This guide breaks down exactly what to look for so you can confidently choose a plan that fits your lifestyle and budget.