technotechies

Maket AI launched an AI Editor that can generate and change a whole floor plan as you type, in real-time. > "Add an office." > "Swap the kitchen and dining room." > "Make the living room bigger." Everyone is an interior designer now 👀

A SINGLE CLAUDE.md FILE JUST HIT #1 ON GITHUB TRENDING. 82,100 stars. 7.8k forks. zero dependencies. Bookmark this before you forget. And your Claude will start working differently. 4 principles. one file. Karpathy's LLM coding habits. distilled. > think before coding. > simplicity first. > surgical edits only. > goal-driven targets before starting. swap it into your CLAUDE.md today. your Claude Code becomes a different tool. Read it today. Link below. Claude → Skills → CLAUDE.md → Better Code → Better Systems → Money

Introducing Exa for Claude: Give Claude access to billions of websites, docs, papers, people, companies, and more. Exa turns Claude into a recruiter, a salesperson, a researcher, a reliable engineer, all with one plugin. Run in terminal: claude plugin marketplace update claude-plugins-official && claude plugin i exa@claude-plugins-official && claude "call mcp__plugin_exa_exa__authenticate (a deferred tool available to you, guaranteed)"

元マッキンゼーの社長が言ってたんだけど、人間が変わる方法って3つしかない。時間配分を変える。住む場所を変える。付き合う人を変える。たったこれだけ。 逆に最も無意味なのが、

so autophagy is basically your cell’s cleaner. a cell gets stressed e.g. when you fast, it starts eating its own broken parts Damaged mitochondria, misfolded proteins et al., Recycled for fuel. here’s where it gets interesting: this cleanup is a double-edged sword.

The quality of your vibecoded slop is horrible. I've seen it. Absolute dogshit. Fortunately, there is a fix. Use this prompt: I want to clean up my codebase and improve code quality. This is a complex task, so we'll need 8 subagents. Make a sub agent for each of the following: 1. Deduplicate and consolidate all code, and implement DRY where it reduces complexity 2. Find all type definitions and consolidate any that should be shared 3. Use tools like knip to find all unused code and remove, ensuring that it's actually not referenced anywhere 4. Untangle any circular dependencies, using tools like madge 5. Remove any weak types, for example 'unknown' and 'any' (and the equivalent in other languages), research what the types should be, research in the codebase and related packages to make sure that the replacements are strong types and there are no type issues 6. Remove all try catch and equivalent defensive programming if it doesn't serve a specific role of handling unknown or unsanitized input or otherwise has a reason to be there, with clear error handling and no error hiding or fallback patterns 7. Find any deprecated, legacy or fallback code, remove, and make sure all code paths are clean, concise and as singular as possible 8. Find any AI slop, stubs, larp, unnecessary comments and remove. Any comments that describe in-motion work, replacements of previous work with new work, or otherwise are not helpful should be either removed or replaced with helpful comments for a new user trying to understand the codebase-- but if you do edit, be concise I want each to do detailed research on their task, write a critical assessment of the current code and recommendations, and then implement all high confidence recommendations.

This article blew my mind multiple times. Did you know, for example, that the widespread view that a woman's fertility is pretty stable until she is 35, after which point it falls off a cliff, is totally FALSE. In fact, women's fertility falls steadily with age after she hits her twenties. The story of how we had the wrong view is quite interesting: Older studies of fertility-age profiles were carried out by going around populations of the world and then seeing how many women had had children in the past year for each age. But of course, women under 35 are often pregnant or postpartum. Women who had had a baby the previous year, or who had got pregnant less than 9 months before the end of the year, would show up in denominator, but in fact could never have got pregnant. Recent data has corrected for this, showing the smooth decline that seems more logical.

*extreme DJ Khaled voice* another one

Researchers just proved that every single elementary function, sin, exp, log, sqrt, comes from one single binary operator. It is like finding the “God Particle" for calculus. In computer science, every complex program breaks down to a single logical operator: the NAND gate. It is the fundamental building block of all digital reality. But for continuous math, physics, engineering, machine learning, we thought we needed a massive toolbox. Addition. Subtraction. Trigonometry. Logarithms. Every scientific calculator and neural network has to juggle all of them. Until today. But this paper proved that every single mathematical function can be generated by a single, bizarre binary operator. eml(x,y) = exp(x) - ln(y). Combine that with the number 1, and you can build everything. Pi. The square root. Sine and Cosine. Arithmetic. It is all just the exact same operator, repeating over and over again in a binary tree. Nobody anticipated this existed. It was found by systematic exhaustive search. But the implications for AI are massive. Instead of an AI struggling to combine different mathematical rules to discover a new scientific law, it can just use a single, uniform architecture. One trainable circuit. One repeatable node. We thought the language of the universe was complex. It turns out, it's just one equation repeating in the dark.

@rileybrown Mimo V2 Pro, then GLM-5.1 and Minimax 2.7, Qwen 3.6 for local!

If you're using Claude Code, start adding this line to your .md file: “Codex will review your output once you are done.” Trust me, you'll get 100x better results.

v3 of @slashlast30days is here. 20,000+⭐ on GitHub. The biggest upgrade yet. An AI agent-led search engine scored by upvotes, likes, and real money - not editors. Reddit comments, X posts, and YouTube transcripts are now FREE. No API keys needed for the core sources. v3 killer feature: intelligent search. Before it searches, a Python pre-research brain resolves X handles, subreddits, TikTok hashtags, and YouTube channels for your topic. It finds the RIGHT places to search before the LLM judge assembles the report. Shout out to @jeffreysperling for building this engine New in v3: - Free Reddit, X, and YouTube (no API keys) - Intelligent pre-research engine - Best Takes (the funniest Reddit comments are first-class) - Cross-source cluster merging - Single-pass comparisons (X vs Y in 5 min, not 12) - GitHub person-mode - ELI5 mode

🚨 ElevenLabs charges $5 to $99/month for AI voice cloning. Their Business plan costs $1,320/month. Someone open sourced a voice AI that clones any voice from a short clip. 30 languages. Studio quality. Free. It's called VoxCPM2. Give it a short clip of anyone's voice. It clones their accent, emotion, tone, and pacing. Then generates any speech you want in their exact voice. 48kHz studio quality. Type "A young woman, gentle and sweet voice" and it creates that voice from scratch. No reference audio. No voice actor. No recording. You describe a voice in words. It builds it. 2 billion parameters. Trained on 2 million hours of speech. 30 languages. One command to install: pip install voxcpm Here's what VoxCPM2 does: → Voice Design: describe any voice in words. Gender, age, tone, emotion, pace. AI creates it from nothing. No reference audio needed. → Voice Cloning: upload a short audio clip. AI clones the voice perfectly. Timbre, accent, rhythm, pacing. → Controllable Cloning: clone a voice AND control the emotion. "Slightly faster, cheerful tone." Done. → Ultimate Cloning: provide audio + transcript. Every vocal nuance faithfully reproduced. → 30 languages. Arabic, Chinese, English, French, German, Hindi, Japanese, Korean, Spanish, and 21 more. No language tags needed. → Context-aware. It reads the text and adjusts emotion and rhythm automatically. News sounds like news. Stories sound like stories. → Real-time streaming. RTF as low as 0.13 on an RTX 4090. Faster than playback speed. → Runs on 8GB of VRAM. → Fine-tune with 5 to 10 minutes of your own audio using LoRA. Build a custom voice model. → 48kHz output. Studio quality. No external upsampler needed. Here's the wildest part: On the Minimax-MLS voice similarity benchmark: → English: VoxCPM2 scores 85.4%. ElevenLabs scores 61.3%. → Chinese: VoxCPM2 scores 82.5%. ElevenLabs scores 67.7%. → Arabic: VoxCPM2 scores 79.1%. ElevenLabs scores 70.6%. A free, open source model is producing more realistic voice clones than a service that charges up to $1,320/month. Professional voice actors charge $250 to $1,000+ per project. AI voice platforms charge $5 to $100/month. Recording studios charge $200/hour. This runs on your GPU. Locally. No API costs. No per-character pricing. No subscription. Free forever. Already hit #1 on GitHub Trending. Built by OpenBMB and Tsinghua University. 2 billion parameters. Apache 2.0 License. Free for commercial use. 100% Open Source.

If you’re actually in the 0.01% in taste, let alone wealth, you can find beautiful but uncrowded places. Let me give some examples of I’ve enjoyed (all within the last couple of years):