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Ambystoma quetzalcoatli is the first fossil salamander to be formally identified in Mexico, revealing that axolotls have inhabited the country for millions of years.

Table of Contents Setting a New Benchmark for Myself My Most Productive Six Months Yet 2...

Cycle recently introduced a separate EU-based control plane, allowing European customers to keep platform management data and telemetry within Europe. The new offering is designed to improve compliance, operational isolation, and responsiveness for European organizations. By Renato Losio

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From order, chaos. From courage, fear. From strength, weakness. — The 36 Stratagems, "Make a Sound...

I’ve wanted to build a text editor for a long time. Not because I thought the world needed another one — it clearly doesn’t — but because editors are one of those projects where you end up touching everything: rendering, input handling, text buffers, undo, plugins, configuration, even OS integration. It felt like the most honest way to learn how these tools actually work. So I finally did. cdin is a lightweight, keyboard-centric text editor with Vim-style modal editing. It started as a fork of lite , but over time it became something more personal. I kept the parts I liked, removed the parts I did not, and reshaped the rest to match the way I actually work. A big reason for that was my computer. I have a weak machine, and that made heavier text editors feel frustrating to use. They were often slow, laggy, or just too much for what I needed. That is how I discovered lite in the first place. It was close to what I wanted, but not quite there. So I forked it, renamed it to cdin, and started making it mine. That meant more than just small tweaks. I removed features I did not need, changed the things that felt awkward, moved from SDL2 to SDL3, and rewired a lot of the project structure along the way. The result is cdin: a small editor built around speed, simplicity, and hackability. The name itself is simple too. cdin means “CODE in”. The code is split between C and Lua. The C side handles the window, renderer, and SDL bindings. Everything else — behavior, plugins, keybindings, config — is loaded in Lua at runtime. That keeps the editor flexible without making it feel heavy. If you want to explore the project, here are the main docs: Overview · Getting Started · Building from Source · Configuration · Vim Keybindings · Plugins · Command Reference There is still a lot I want to improve, but cdin already feels like something that belongs to me in a way no other editor ever did. If you check it out, please leave a star, fork it, or send an Issue or PR if you find a bug or wa

I open sourced a project I have been building on the side: a Go MCP server that connects Claude Code (or Cursor) directly to a live PostgreSQL database. Repo: github.com/gupta-akshay/postgres-mcp The problem it solves Most "AI plus database" workflows still look like this: copy SQL out of a chat window, paste it into a DB client, run it, copy the output back. It breaks flow, and the assistant never sees your actual schema, so it guesses. MCP fixes the connection problem. This server is what sits on the other end for Postgres. What it does The server exposes nine tools over MCP: Schema introspection - real tables, columns, indexes, constraints execute_sql - run queries directly (read only in restricted mode) explain_query - EXPLAIN ANALYZE, including against a hypothetical index get_top_queries - pull slow queries from pg_stat_statements Index advisors - recommend indexes using a greedy Database Tuning Advisor built on hypopg analyze_db_health - vacuum, XID wraparound, replication lag, invalid indexes, and more, checked in parallel That means you can ask "why is this query slow" and the assistant actually runs the EXPLAIN, checks the stats, and can simulate an index before anyone touches the schema. Why Go The project is inspired by the Python crystaldba/postgres-mcp . I rebuilt it from scratch in Go so it ships as a single ~15 MB static binary. No Python runtime, no dependency chasing. docker build , point Claude Code at it, done. Restricted mode wraps every call in a read only transaction, so write protection comes from Postgres itself, not string matching on the query text. Where to look The repo has the full setup instructions, the Docker config, and the test suite (unit, integration, and end to end against a real Postgres container with pg_stat_statements and hypopg ). CI fails under 95% coverage. If you spend real time in Claude Code or Cursor and also spend real time worrying about Postgres performance, take a look: github.com/gupta-akshay/postgres-mcp I wrote

Hi everyone, My Microsoft SWE internship is coming to an end, and I have my technical exit interview/final evaluation coming up. If you've gone through this interview before, could you share your experience? Some questions I have: What kind of technical questions were asked? Was it mainly DSA, OS,DBMS What difficulty level should I expect? Any tips on what I should focus on during the last few days of preparation? I'd really appreciate hearing about your experience. Thanks in advance! submitted by /u/CabinetFamous4731 [link] [留言]

Every meditation app I have tried wants something from me. Headspace wants me to maintain a streak. Calm wants me to listen to a Daily Jay. Insight Timer wants me to join a group. One after another, apps designed to reduce my stress started creating new forms of it. The Feature Trap Here is what happened to meditation apps between 2015 and 2026: 2015: "Just meditate 10 minutes a day." 2018: "Track your streak! You do not want to break it, do you?" 2021: "Compare your stats with friends. See who meditated more this week." 2024: "AI-generated personalized guided meditation based on your emotional state, delivered at the optimal time based on your circadian rhythm." Wait — was not the whole point to stop optimizing everything? Subtraction as a Feature I switched to OneZen last month. Here is what I noticed: No onboarding. Open the app. Breathe. Close the app. That is the entire user flow. No streaks. I missed three days last week and the app did not shame me. It did not even notice. It just opened to the same calm screen, waiting, as if three days was the same as three hours. No gamification. No XP points. No badges. No "you are in the top 14% of meditators this month." Because meditation is not a competition you can win. What Subtraction Feels Like The first week was uncomfortable. I kept checking if I had "done it right." There was nothing to check. No dashboard. No stats. Just me and my breath. By week two, something shifted. Meditation stopped being a task on my to-do list and started being... just breathing. I was not practicing to maintain a number. I was practicing because it felt good. This is what minimalism actually means. Not fewer pixels. Less cognitive load. Less obligation disguised as features. The Bigger Idea OneZen's philosophy applies far beyond meditation apps: The best productivity tool is the one with the fewest notifications. The best social network is the one that respects when you leave. The best habit tracker does not exist — because the ha

I'm a 20-year-old computer science student leading the development of a software project called Skyline Computer World. Rather than rushing into features, I decided to start with the architecture: designing the database, setting up NestJS, PostgreSQL, Prisma, and establishing a modular backend structure. The process has involved plenty of debugging, redesigning, and learning—from Prisma migrations to project organization—but it's reinforced how important a solid foundation is for long-term maintainability. I'd be interested to hear from more experienced backend engineers: What architectural decision had the biggest long-term impact on one of your projects? If you were starting a backend from scratch today, what would you do differently? submitted by /u/amjakez [link] [留言]

📖 Read the full version with charts and embedded sources on ComputeLeap → You can now buy a walking, flipping, kung-fu-kicking humanoid robot on AliExpress for $4,900 — less than a used Honda Civic, less than a semester of community college, less than what most people spend on a couch-and-TV combo. Unitree's R1 AIR shipped its first global batch in April, and it represents something the robotics industry has been promising and failing to deliver for decades: a humanoid robot that a normal person can actually afford. But here's what the breathless headlines won't tell you: price is falling faster than capability. The gap between what this robot costs and what it can actually do is where the hype lives — and understanding that gap is the difference between seeing a revolution and seeing a very expensive toy. The Number That Matters The Unitree R1 AIR stands 4 feet tall, weighs 55 pounds, and packs 20 degrees of freedom into a bipedal frame that can run, do cartwheels, throw punches, and execute spin kicks . At CES 2026, Unitree's booth stopped traffic with R1s replicating Bruce Lee sequences, Michael Jackson dance moves, and Mike Tyson combinations. The base R1 AIR ships with a monocular camera, 8-core CPU, and onboard AI for voice and image recognition. For $1,000 more, the standard R1 at $5,900 adds six more degrees of freedom (26 total), binocular depth perception, waist articulation, and head movement. Both come with hot-swappable batteries — about an hour of runtime per charge. To put the price in context: Figure AI and Tesla each shipped roughly 150 humanoid units in 2025. Unitree shipped 5,500 . That's not a typo — Unitree alone outshipped every Western humanoid manufacturer combined by a factor of 20x. The R1's $4,900 price point isn't an outlier. It's the leading edge of a Chinese manufacturing tidal wave. The Raspberry Pi Parallel — and Its Limits When the Raspberry Pi launched in 2012 at $35, it didn't replace laptops. It didn't become the computer most peo

In the field guide I covered what an AGENTS.md is and what belongs in it. This is the hands-on follow-up: we'll build a complete AGENTS.md for a real project, one section at a time, then point an AI coding agent at it and watch the difference it makes. By the end you'll have a working file — and you'll have seen it pay off. New to AGENTS.md? It's a single Markdown file at the root of your repo that tells AI coding agents how to work in it — build steps, tests, conventions, guardrails. The "why" behind each section is in the field guide . The project we'll use We'll write the AGENTS.md for a small but real service: a URL shortener API in Python — FastAPI, SQLite, pytest. A couple of endpoints, a thin data layer, a test suite. Follow along with this, or swap in your own repo — the steps are identical. Its shape: linkshort/ app/ main.py # FastAPI routes db.py # SQLite access models.py # Pydantic models migrations/ # generated SQL — not hand-edited tests/ requirements.txt Step 0 — Start with an empty file At the repo root: touch AGENTS.md That's the whole step. We'll fill it in one section at a time, building toward a file an agent can read in thirty seconds. Step 1 — Orientation: one line Tell the agent what it's looking at. Add: # AGENTS.md A URL shortener API in Python — FastAPI, SQLite, pytest. One sentence sets the agent's priors: it knows the language, framework, and storage before it reads a single line of code. Step 2 — Setup and run The agent can't help if it can't start the project. Add the real, copy-pasteable commands: ## Setup python -m venv .venv && source .venv/bin/activate pip install -r requirements.txt ## Run uvicorn app.main:app --reload # http://localhost:8000 Use the commands that actually work in your repo — no placeholders. Step 3 — Tests: the agent's feedback loop This is the most important section, because tests are how the agent checks its own work. Add: ## Test — all must pass before a change is done pytest ruff check . mypy app Now the agent

My wife tracks her meals, and I watched her type "buckwheat, boiled, 100 g" into a calorie app for the hundredth time. Search, scroll, pick the wrong entry, fix the grams. Every meal, every day. At some point it's easier to teach a vision model to look at the plate. So I built a Telegram bot. You send a photo of your food, it identifies the dishes, estimates portion weights, and replies with a card: calories, protein, fat, carbs. Text and voice work too ("2 eggs and a toast"). The borscht incident The first version was hilariously confident about wrong answers. Borscht — a red beet soup, if you've never met one — came back as "berry compote" (a sweet berry drink). Red liquid in a bowl, what else could it be? Adding more example dishes to the prompt made it worse : the model just got magnetized to whatever was on the list. A cod fillet became "syrniki" (cottage cheese pancakes) because syrniki were mentioned and both are pale and pan-fried. What actually fixed it was making the model read the serving context before naming anything: liquid served in a deep bowl with a spoon and sour cream is soup, not a drink. Flaky texture that separates in layers is fish, not pancakes. Fried items are never served floating in liquid. A short list of physical rules beat a long list of dishes. Portion estimation works the same way — the model reasons from plate size, cutlery, how full the bowl is. My wife has been checking its gram estimates against her kitchen scale for a week and it lands closer than either of us expected. Stack, briefly Python + aiogram, a vision LLM with structured JSON output (with a fallback parser for the days the model decides to wrap JSON in prose), Pillow for rendering the result cards. Photos are analyzed on the fly and never stored. Payments are Telegram Stars, so there's no app store, no signup, no card form — the whole onboarding is "send a photo". Yesterday I also wired up inline mode: type @SnapPlateBot in any chat, describe the food, and it counts rig

Introduction: The Illusion of Productivity Metrics Traditional software development metrics—velocity charts, commit counts, bundle size—are the comfort objects of the coding world. They sit on dashboards, glowing with the promise of insight, but in reality, they’re often lagging vanity numbers . They don’t capture the narrative of a week’s work; they don’t reveal the decisions , the reversals , or the patterns that define progress. Instead, they deform the truth by oversimplifying it, much like a rubber band stretched too thin—it snaps under pressure, failing to hold the complexity of real work. Consider the mechanical process of a commit. A commit is a snapshot , a frozen moment in time. But software development isn’t a series of snapshots; it’s a sequence . When you string commits together without context, you miss the heat of decision-making—the back-and-forth, the undoing, the redoing. This is where traditional metrics fail. They don’t account for the thermal expansion of ideas, the way a decision made on Monday might cool by Friday, only to be reheated and reshaped. Without a narrative, these metrics are like a machine running without lubrication: they friction against reality, wearing down under the weight of their own inadequacy. The Mechanism of Metric Failure Let’s break down the causal chain: Impact: Developers rely on metrics like commit counts to gauge productivity. Internal Process: These metrics are lagging indicators , reflecting past actions without context. They don’t capture the why behind the numbers—the decisions, the reversals, the thought process. Observable Effect: Developers miss critical patterns, such as repeated decision reversals, leading to inefficiencies and missed opportunities for improvement. It’s like trying to diagnose a car’s engine by looking only at the speedometer—you’ll never catch the misalignment in the gears. Narrative-Driven Insights: The Optimal Solution Contrast this with a narrative-driven approach . When you narrate a

Why I'm Building the Fast Series I'm building the Fast Series because creator software has gotten too complicated. Plenty of tools are powerful, but they make you fight the software before you can make anything. You want to record a tutorial, stream a game, clip a useful moment, compress a file, or turn an idea into a short video. Instead, you're digging through settings, codecs, plugins, device permissions, export presets, and cryptic error messages. That's the problem I keep running into, and the Fast Series is my attempt to solve it: practical Windows software where each tool does one job clearly and reliably. Not everything needs to be a giant all-in-one platform. Sometimes the better product is a small tool that opens quickly, gives you sensible defaults, explains what's happening, and gets out of your way. That's the direction I'm taking with Sturm Technologies. The Problem With Creator Tools There are already great tools for recording, streaming, editing, clipping, and compressing. OBS is powerful. Professional editors are powerful. FFmpeg is powerful. There are cloud tools, browser tools, AI tools, and creator suites that promise to do everything. But power is not the same thing as clarity. Most creators don't want to become experts in capture APIs, bitrate math, encoder settings, audio routing, or export pipelines. They want to make something and publish it. The pain usually shows up in small moments. You record a video and the audio is missing. You compress a file and it still doesn't meet the upload limit. You spend more time scrubbing a long video than actually clipping it. You hit an error and the app hands you a technical dump instead of telling you what to fix. That's where I think there's room for better software. Not bigger software. Better software. Start With FastCast The first product in the series is FastCast , a Windows recording and streaming app for people who want OBS-level practicality without OBS-level setup. FastCast focuses on screen cap

Over the past week, the AI hardware news I've been tracking adds up to more than $610 billion in capital deployed globally — in just seven days. Not valuations. Not market cap. Actual capital expenditure commitments. Korea $550B, Japan $6B, Qualcomm's new accelerator, Kawasaki Heavy Industries' $1B AI infrastructure bond — this round of moves has already surpassed the wildest half-year of the 2000 dot-com bubble in scale. But this time the money isn't flowing into web pages. It's flowing into chips, memory, and power. Watching all of this over the past few days, I've been thinking: for investors and for builders like us making products on top of AI, what does this gamble actually mean? The Real Story Behind AI Training Bottlenecks: From GPU Scarcity → Memory Scarcity → Power Scarcity Honestly, everyone watches AI through the lens of models, but the real bottleneck was never the models — it's been the hardware. From 2023 to 2025, the bottleneck shifted from GPU scarcity to memory scarcity, and is now pushing toward power scarcity. When GPUs were tight, everyone scrambled for H100s and NVIDIA raked it in — but the part that actually throttled the H100 wasn't the GPU core, it was the HBM high-bandwidth memory. On the B200, the HBM3E stacked on top has its capacity locked up entirely by NVIDIA at SK Hynix, while Samsung is chasing hard but its yields can't keep up. That's why South Korea just committed $518B to build 4 memory fabs plus $52B for the central regions, totaling $550B ( TechCrunch ). This isn't just about filling upstream capacity — the key is that Samsung + SK Hynix are trying to flip themselves from being NVIDIA's downstream suppliers into becoming the dominant players in AI hardware. Why did downstream hardware investment kick off so late? Because for the past two years people were still watching and waiting to see if "this AI hype cycle would cool down again." By 2026, GPT-6, Claude 4, and Gemini 3 are all live, inference costs have come down, user numbe

A gente sempre ouve falar que o sistema operacional impede que um processo veja a memória do outro ou que o programa fale diretamente com o hardware, mas normalmente não explicam o "como". Eu sempre achei isso meio mágico até que eu resolvi ir atrás da resposta, e é bem interessante. Vou me basear na arquitetura x86, mas é provável que outras arquiteturas sejam parecidas. O problema: a CPU Pra CPU não existe processo, kernel, sistema operacional. Existe só endereços de memória de onde ela lê a próxima instrução e executa. Se a CPU pode falar direto com a RAM, SSD, teclado, mouse, tela... O que me impede de escrever um programa pra ler suas senhas e tokens direto da RAM? Ou de ler arquivos e alterar arquivos sensíveis direto no SSD? Por outro lado, se o kernel fiscalizasse cada instrução que da CPU antes dela executar, isso seria extremamente lento... Outro problema: os interrupts Se a CPU só executasse sequencialmente, seu sistema poderia executar várias coisas e esquecer de checar se uma tecla foi apertada, se o mouse mexeu, etc... Então certos eventos interrompem o que quer que a CPU esteja fazendo para serem tratados assim que possível. Alguns exemplos de interrupt são: Teclas do teclado pressionadas ou soltas Botões e movimento do mouse Timers Operações de disco assíncronas Pacotes de rede recebidos/transmitidos Uma solução: rings Os processadores da arquitetura x86 tem o esquema de rings. Pense em rings como grau de limitação. Ring 0 significa limitação zero, ou seja, acesso a todas as instruções da CPU e consequentemente acesso total ao hardware e memória. O kernel roda em ring 0, ou kernel mode. O kernel assim que é carregado configura todos os interrupts handlers da CPU para executar o handler apropriado do kernel, em kernel mode, claro. Em ring 3 a CPU fica limitada e não pode fazer instruções consideradas privilegiadas. E obviamente em ring 3 a CPU não consegue se colocar em ring 0 sozinha, pois dessa forma qualquer programa conseguiria se pôr em ring 0. O

The Problem That Wouldn't Leave Me Alone Pakistan has 220 million people. A functioning legal system. Hundreds of Acts, ordinances, and constitutional provisions that technically protect every citizen. Almost nobody can use them. The median lawyer's consultation fee in Karachi is more than what many families earn in a week. Legal aid is understaffed and geographically concentrated in major cities. And the laws themselves? Written in English — a language most of the population reads functionally at best, and doesn't speak at home at all. So when a landlord illegally locks someone out. When a factory worker gets fired without severance. When a woman wants to know her inheritance rights. When a tenant needs to understand what "Section 16 of the Rent Restriction Ordinance" actually means for their specific situation — they either find a lawyer they can't afford, ask someone who doesn't really know, or quietly give up. This isn't a knowledge problem. It's an access problem. I'm a CS student at Sukkur IBA University in interior Sindh — not Karachi, not Islamabad. The kind of city where you feel the gap between what the law says and what people actually know it says every single day. That gap is where HAQ started. HAQ is an Arabic and Urdu word. It means right — as in, what is rightfully yours. The name felt important. The Core Idea: Ask the Law, Get the Law There's a specific failure mode with AI and legal questions that drove every design decision I made, and it's worth naming clearly. Standard LLMs — any of them — will answer legal questions confidently. They'll cite "Section 144" or "the Transfer of Property Act" with total authority. They are often wrong. Sometimes subtly: the section exists but doesn't say what the model claims. Sometimes obviously: the Act doesn't apply in that province. Always uncitable: the user has no way to verify without finding the source themselves. For an accessibility tool, a confidently wrong answer isn't neutral. It's actively dangerous.

Half a year ago, I wanted to see for myself what can we currently have with local LLMs. I went down the rabbit hole, learned quite a lot in the process, and shared my results in an article . The results were pretty discouraging: even with 32 GB VRAM, the best models I could run were both too slow and too dumb. At the same time, what you could get for free from inference providers was actually decent - and much faster. I remember my conclusion: "Let's wait for the next generation of models, which looks very promising. If we can run something comparable to full-size Qwen3-Coder-480B locally, that would be year of the Linux Desktop age of fully capable local LLMs. And now this day has arrived. Models Half a year later, I'm revisiting this question. And this time, the whole situation has turned upside-down. Almost none of the providers still have free tier, and anything that's still free is barely good enough even for the simplest tasks. And is rate-limited all over. And on the local side, the next Qwen lineup is out. So, that's what I'm going to be looking at. Once again, I have two RX6800's, 16 GB each, and 64 GB RAM. On one hand, this is more VRAM than any "normal person" can have with one GPU - unless you've got something specifically for AI, like an unified-memory Mac or a DGX Spark. On the other hand, RX6800 is "pre-AI" - anything newer will have much better performance thanks to tensor processors. Qwen3.6-27B : This is a dense model, so basically you can't run it at all on anything less than 32 GB VRAM. It's the slowest one, but also the best one if you can run it. Its accuracy is claimed to be on par with Claude 4.5 Opus, and better than Qwen3.5-397B-A17B . This is what I've been waiting for. It runs reasonably fast on my setup, so it's very much usable both in terms of performance and accuracy. Qwen3.6-35B-A3B : This one is MoE, and it's pretty small, so it's the fastest one. It's good for anything that doesn't require too much (i.e. for agentic tasks that don'

In production we have ~1.5 TB of full-text court decisions and their vector embeddings, plus another ~550 GB of other legal data: registries, legislation, business entities, a Spanish case law corpus, EU-Lex. If we take this corpus and train an MoE model the size of DeepSeek V3, scaled to 860B parameters, on GCP — what comes out? We break down the dataset, architecture, compute cost, and the properties such a model would have on Ukrainian law. What's in the Dataset The entire corpus is what's already running in SecondLayer's production. No extra scrapes, no Common Crawl, no noise. EDRSR — the dataset core, ~1.5 TB. The Unified State Register of Court Decisions of Ukraine. 96.2 million full-text decisions (1,079 GB in PostgreSQL TOAST), 471 GB of vectors in Qdrant (voyage-3.5, 1024-dim), 28 GB of metadata (court, judge, date, case category, proceeding type, statute code). Breakdown by jurisdiction: civil 33.7M, administrative 14M+, criminal 12M+, commercial 6M+, misdemeanors 6M+. Largest annual cohort — 2024 (115 GB of TOAST text). OpenReyestr — 43 GB. Ukrainian public registries: 16.7M legal entities (EDR), ownership structures (beneficiaries, shareholders), debtors (State Enforcement Service), NAIS registries. This is the foundation for SneakyPiper — our due-diligence platform — but here it serves as raw corpus for the model. Legislation — ~40 GB. The Constitution, major codes (Civil, Criminal, Criminal Procedure, Civil Procedure, Commercial Procedure, Administrative Procedure, Labor, Tax, Customs), laws, and secondary legislation. All structurally annotated: articles, parts, clauses, revision dates with effective-date tracking. This isn't flat text: we know that Article 124 of the Constitution took effect on a specific date, carries particular references, and is cited in a precise number of decisions. Supreme Court review practices + lu_court_decisions — ~25 GB. SC plenary decisions, practice overviews, Grand Chamber rulings. This is the most valuable slice — the