标签：#Go

Treasury Secretary Scott Bessent said the U.S. could sanction Chinese open AI models over alleged IP theft, expanding the Trump administration's campaign to slow China's AI advances.

Google is launching Gemini 3.6 Flash alongside a new security model dedicated to quickly finding and patching security vulnerabilities. In a blog post on Tuesday, Google describes Gemini 3.5 Flash Cyber as a "cost-efficient and highly capable alternative" to larger, more expensive AI systems, such as the one offered by Anthropic's Mythos. The cybersecurity model […]

The new U.K. government scrapped a digital ID program that millions of British people opposed, in place of a tax cut to tackle the cost of living.

It’s a lot : According to information obtained by The Tech , MIT is spending over $3 million on more than 500 AI surveillance cameras in academic buildings, residence halls, and outdoor areas along Memorial Drive. Installation of the new cameras, along with the wiring and infrastructure that will support them, began November 2025 and will likely continue until September 2026. Technical specifications for the cameras suggest that they will be capable of collecting real-time face and object classification data, including detection of motion, loitering, crowds, face masks, and camera tampering. Individuals can also be automatically classified on the basis of clothing color, gender, and age, up to a distance of 35 feet (11 meters) from the camera. According to a statement from MIT spokesperson Kimberly Allen, any collected data is “retained up to 30 days,” unless an exception is granted...

Members of the Army received an email informing them that they were rapidly depleting their AI tokens, and needed to limit use.

Once a quirky bastion of amateur vulva jewelry and pet portraits, Etsy is now deluged with mass-produced goods and AI knockoffs. Some customers don’t seem to mind.

If you are a fellow blogger or web developer looking to build a high-performance, developer-friendly,...

The first hour with a new repo is archaeology: open package.json , skim the Makefile, hunt for .env.example , grep workflows for secrets. , check Compose for ports. I already built focused tools for each of those questions. What I still wanted was one command that prints the whole map. coldstart coldstart is that overview — offline, no accounts, agent-friendly JSON. go install github.com/SybilGambleyyu/coldstart@latest coldstart coldstart -md >> ONBOARDING.md coldstart -json It reports: Runtimes — Node engines, Go version, Cargo edition, Python requires, .nvmrc , .tool-versions Run — preferred npm scripts, Makefile ## targets, just, Taskfile Ports — Compose, env *_PORT , script --port Env keys — from .env.example CI secrets/vars — from GitHub Actions workflows (builtins like GITHUB_TOKEN omitted) Overview vs drill-down Need Tool One-pager coldstart Full task map howrun Every port + live check projports Secrets vs gh secret list needsecrets Lockfile PR summary lockbrief Typical first hour: coldstart # then only if you need depth: howrun -f test projports -unique -check needsecrets -check Small binaries, MIT, no SaaS. If it saves a README scroll, it is working.

Measured 2026-07-21 on vllm/vllm-tpu:nightly (vLLM 0.23.1rc1.dev1076), a GCE flex-start ct6e-standard-1t (one TPU v6e chip, 32 GB HBM) in europe-west4-a. TL;DR The plain google/gemma-4-E2B-it serves well on one v6e chip; none of its QAT siblings load at all. The 2-billion-parameter "efficient" Gemma 4 sustains 213 tok/s for a single user with a 16 ms first token, scales to ~2,200 output tok/s across concurrent streams, handles OpenAI-style function calling — including parallel calls and refusal to hallucinate calls — without a miss, and answers simple vision questions accurately in ~200 ms. The QAT variants are a different story: the int4 compressed-tensors export hits an unimplemented quantization path, and the bf16 QAT export trips a loader bug — the Gemma 4 implementation demands per-layer norms that E2B's KV-sharing architecture legitimately doesn't have. Filed upstream as tpu-inference #3225 . One capability coupling to know about: with a reasoning parser configured, schema enforcement only engages when thinking is enabled — thinking-off requests sail through unconstrained with a 200 status. Config interaction, not TPU limitation; details below. 1. Getting to a serving endpoint The host is a GCE flex-start VM — capacity granted on request, billed until deleted, hard-stopped at a 4-hour max run, $1.35/chip-hour. A startup script installs Docker, pulls vllm/vllm-tpu:nightly , fetches the Hugging Face token from Secret Manager via the metadata server, and launches vLLM. Boot timeline: VM RUNNING at t+0 (200 GB boot disk — the 10 GB default cannot hold the vLLM image) → Docker installed ~t+1:00 → image pulled ~t+6:00 → weights downloaded, XLA compiled, health green ~t+8:30. Two environment quirks worth knowing: Direct SSH silently times out on some networks even when the VPC allows tcp:22 — the block is upstream of the VPC. IAP tunneling ( gcloud compute ssh --tunnel-through-iap ) rides over HTTPS and works; so does tunneling the API port with gcloud compute start-

If you have written any optimization code, you have met linear programming even if nobody called it that. "Maximize output without blowing the resource budget" is an LP problem, and the classic algorithm that solves it is the simplex method. It is worth understanding not because you will hand-code it (you'll usually call a solver), but because knowing how it moves makes you far better at modeling problems for it. Here is the algorithm stripped down to its logic. The problem shape Every LP problem has three parts: an objective function to maximize or minimize, e.g. Z = 5x1 + 4x2 a set of linear constraints, e.g. 6x1 + 4x2 <= 24, x1 + 2x2 <= 6 non-negativity: all variables >= 0 Geometrically, the constraints carve out a feasible region (a polytope). The optimum always sits at a corner of that region. The simplex method is just a smart way of hopping from corner to corner, uphill, until there is no higher corner to move to. The algorithm as pseudocode build initial tableau (add a slack variable per <= constraint) loop: compute Cj - Zj for each column if all (Cj - Zj) <= 0: break # optimal reached pivot_col = column with most positive Cj - Zj # entering variable ratios = RHS / pivot_col entries (only positive entries) pivot_row = row with smallest non-negative ratio # leaving variable pivot(pivot_row, pivot_col) # elementary row operations return solution from final tableau That's it. Four moves per iteration: score the columns, pick the entering variable, run the ratio test for the leaving variable, pivot. Repeat until the optimality condition holds. A quick worked run Take Maximize Z = 5x1 + 4x2 subject to 6x1 + 4x2 <= 24 and x1 + 2x2 <= 6. Add slack variables s1, s2, build the tableau, and iterate. The optimum lands at x1 = 3, x2 = 1.5, Z = 21. Two pivots and you're done. Simple on paper until the numbers get ugly. Where humans (and debugging) actually break The algorithm is clean. The arithmetic is not. A single wrong entry in one pivot silently corrupts every table

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry . This is our official submission for the DEV Big Summer Bug Smash challenge under the #bugsmash track. Below is the technical tale of how we isolated, debugged, and optimized cross-layer node latency issues when deploying our Web3 framework on Polygon. The Problem: The Post-Hard Fork RPC Latency Wall 🐛 During heavy network volume spikes or directly following major ledger upgrades, our automated event listener logging pipeline kept crashing with random, non-deterministic invalid block range exceptions when attempting to pull historical data blocks via standard eth_getLogs routines. The Technical Root Cause The root bottleneck came down to an internal desync inside shared public RPC telemetry environments: The Bor Layer mints new block headers at a blistering speed (~2 seconds). The Internal Indexer DB takes slightly longer to completely unpack, parse, and commit transaction event logs to disk. When our asynchronous scripts called the node, latest grabbed the bleeding edge tip of the chain from memory, but a simultaneous getLogs query hit the slower indexer database. This split-millisecond race condition threw immediate pipeline errors. The Fix: Layered Application Buffering 🛠️ To smash this bug without modifying low-level node client builds, we engineered a programmatic block-padding delay loop directly into our interaction routers. Instead of tracking unfinalized tip block states blindly, we forced our queries to target safe block ranges sitting securely just behind the tip of the chain. // Localized block-buffer deployment fix const currentChainTip = await provider . getBlockNumber (); const indexedBlockBoundary = currentChainTip - 3 ; // Buffer 3 blocks (~6 second safety zone) const targetLogs = await contract . getLogs ({ fromBlock : indexedBlockBoundary - 20 , toBlock : indexedBlockBoundary }); This structural adjustment completely stabilized our off-chain reward data pipeline, gua

The director role for the Center for AI Standards and Innovation (CAISI) has become a revolving door since David Sacks left his position as czar.

Alphabet, Google's parent company, is reportedly working on a new chip designed to make its Gemini models run much more efficiently.

A simple tutorial series on go in Hinglish. Jitna mujhe aata hai utna, aur zyada tar mere notes se! Note: Me non-native speaker hu to spelling mistakes to honge, is liye Hinglish spellings aur grammar ke liye main AI use karunga, par dev ke bot ko pata nahi chalega kyuki ye English nahi hai 😉 Parichay / Intro Shuruat hum isse karenge ki Go kya hai aur uske fayde kya hain. Pehle hum Wikipedia ki definition dekhte hain: To, Go ek aasan language hai. Go compiled language hai, matlab ek bar compiler ko humne apna code diya to wo ek executable file dega jo sidha hum chala sakte hain bina kisi aur dependency ke, jaise ki Python ka interpreter. Dusri high-level languages jaise Python ya JavaScript se Go tez chalta hai. Garbage collected hai, yani C ki tarah hame memory ko manually manage karna nahi padega. Go ka istemal kahan hota hai? APIs aur Web Servers banane me Network Programming aur Distributed Systems me Cloud-Native Applications aur Microservices banane me DevOps aur Infrastructure Automation me Command Line (CLI) Tools banane me Agar upar ki baatein abhi samajh na aayein, to koi baat nahi. Filhal itna samajh lijiye ki Go ka istemal bahut jagah hota hai to job ke liye kaam aayega. To ye thi Go ki kahani, CSM ki zubani... (jo chalti rahegi!) Aur haan, har post ke aakhir me meri ek shayari hogi, jo aapko guru dakshina ke roop me jhelni padegi! 😄 ख़यालों को बातों में उलझाए रखना । इस पल के हक़ीक़त को सुलझाए रखना । शिद्दत से चाहा जो मिल कर रहेगा । चेहरे पे मुस्कान थोड़ी बनाए रखना ।। -csm

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Over the weekend, several current and former advisors to President Donald Trump on AI publicly lobbed insults at the country’s leading AI companies. David Sacks, the president’s AI and crypto “czar” until…

Online retailer AliExpress says it's shocked by largest DSA fine yet.

Introduction At recent technology conferences, one topic has caught my attention: every year, more health-focused devices, sensors, and applications appear. Smartwatches track heart rate, smart scales measure body data, glucose monitors record blood sugar levels, and apps help users track sleep or nutrition. Today, the amount of information we can collect about our own bodies is enormous. This article was inspired by an everyday experience with my father, Herminio ❤️ . Whenever he has a medical appointment, he opens the Apple Health app and shows the doctor the evolution of his heart rate, physical activity, sleep hours, and other recorded metrics. While watching this, I kept asking myself the same question: are we really making the most of all this information? Showing a chart during a medical appointment can be useful, but the data could provide much more value if it were automatically processed, summarized, and transformed into a structured health report. For this reason in this project, I use Gemini to transform previously calculated metrics into a clear and organized summary. The LLM does not analyze all the raw records or perform the main calculations. The pipeline processes the data, calculates the indicators, and generates the visualizations, while the model acts as a support layer for building the report narrative. The goal is not to create a medical application or replace professional judgment. Instead, the purpose is to build a prototype that shows how Apple Health exports, deterministic data processing, visualizations, and an LLM can be combined to generate automated reports. This project was developed using simulated data from three patients, so the complete pipeline can be reproduced without using real clinical information. ✨ Why Gemini? This project uses an LLM to transform previously processed metrics into a structured narrative that can be reviewed more easily by a healthcare professional. I chose Gemini for practical reasons: 〰️ I was already famil

Tres semanas después de publicar un sitemap dinámico que orgullosamente listaba cada perfil de miembro, Google Search Console me dijo que esos perfiles eran un error. No con un error de plano, sino con dos veredictos más callados: Soft 404 y Duplicada sin canónica seleccionada por el usuario . Esta es la historia de leer ese reporte, separar el ruido de la única señal real, y el arreglo, que fue quitar páginas del índice, no agregarlas. TL;DR El reporte "Por qué las páginas no se indexan" de Google es ~80% benigno por diseño. Aprende a triarlo o vas a perseguir fantasmas. Mis perfiles de miembros salieron marcados como Soft 404 (uno) y Duplicada sin canónica seleccionada por el usuario (otro). La misma causa raíz: el perfil público anónimo es deliberadamente flaco, un esqueleto con el username, todo lo demás es PII oculta a quien no ha iniciado sesión. Flaco + casi idéntico entre usuarios se lee como "página vacía" y "clúster de duplicados". No puedes arreglar un Soft 404 enriqueciendo una página que por contrato no tienes permitido enriquecer. Así que el arreglo es noindex,follow en la captura, más sacar las URLs del sitemap (un sitemap que lista una URL noindex es una autocontradicción que Google va a señalar). El modelo mental en una línea: una página que a propósito no tiene contenido para los visitantes anónimos no tiene nada que hacer en un índice construido para visitantes anónimos. El reporte que lo empezó todo El reporte de cobertura del índice, ordenado por número de páginas: Razón Fuente Páginas Descubierta, actualmente sin indexar Sistemas de Google 55 Duplicada sin canónica seleccionada por el usuario Sitio web 9 Página alternativa con etiqueta canónica correcta Sitio web 3 Página con redirección Sitio web 2 Rastreada, actualmente sin indexar Sistemas de Google 2 Soft 404 Sitio web 1 Excluida por etiqueta 'noindex' Sitio web 1 No encontrada (404) Sitio web 1 Bloqueada por acceso prohibido (403) Sitio web 1 Noventa y tantas URLs "sin indexar". El instint

When a new video shows up in one of our regional crawlers, three things need to happen almost immediately: the SQLite FTS5 search index for that region needs a new row, the discovery ranking cache needs to be invalidated, and the sitemap generator needs to know a URL was born. For a long time we did all of this inline, inside the same cron process that fetched the video. It worked until it didn't. A slow FTS5 rebuild would stall the fetch loop, a sitemap write would fail silently, and a crash halfway through meant one region had the video indexed and another didn't. The fetch and the fan-out were fused together, and every failure was a partial failure. The fix was to stop treating "a video was discovered" as a function call and start treating it as an event. At TrendVidStream we run discovery across 8 regions, and the moment we introduced NATS JetStream as the spine between the crawler and the downstream consumers, the whole system got calmer. This post is the concrete version of how we did it: the stream config, the publishers, the consumers, and the mistakes we made that you can skip. Why Not Just Use a Queue Table in SQLite We already had SQLite everywhere, so the obvious move was a jobs table. We tried it. The problems showed up fast: Polling latency vs. load tradeoff. Poll every second and you hammer the DB with mostly-empty SELECT queries across 8 regions. Poll every 30 seconds and your search index lags noticeably behind your crawler. No fan-out. One row, one worker. If the sitemap generator and the FTS5 indexer both need the same event, you either duplicate rows or invent a consumed_by bitmask. Both are ugly. Locking. SQLite's writer lock means the queue table and the actual data table start contending under the multi-region cron bursts we run. Cross-region delivery. Our regions aren't all on the same box. A queue table doesn't cross machines without you building a replication story on top. JetStream solves all four: push-based delivery (no polling), multipl

I regularly lose time to terminal muscle memory. I work across Windows and Unix-like shells, so I will remember the right command in the wrong environment, transpose a Git subcommand, or use a valid binary with an invalid subcommand. The fix is usually easy to find. The interruption is the costly part: stop, search, translate the answer back into the current shell, and try again. The idea is not new. There are projects that help with failed commands, as well as terminal-integrated LLMs such as aichat. However, I wanted something that does one thing well: fix commands in a seamless workflow powered by an LLM. I wanted a deliberately narrow tool: when a command fails, suggest the command I probably meant, let me inspect it, and run it only after confirmation. During a Fable promotion period, I used an LLM coding agent to see how far a well-scoped prompt could get me. With very little steering, it produced a usable Go prototype in roughly an hour. The project is now open source: nudge . The workflow I wanted The core interaction is intentionally small: Run a command as usual. If it fails, type fix (or bare nudge ) to get a suggested correction. Review it, then press Enter to run, e to edit it first, or n to cancel. The cheapest case is a plain typo, which never reaches a model at all: PS> git pshu git: 'pshu' is not a git command. See 'git --help'. PS> fix `git pshu` isn't a valid command. Did you mean: → git push (typo fix for `git pshu`) Run it? [Enter = yes / n = no / e = edit] The (typo fix for ...) label is the tool telling me it answered locally, in under 10 ms, without a network call. The cross-shell version of the same mistake is reaching for a binary that does not exist here. Because the binary is missing, the shell's command-not-found hook fires and I do not have to type anything at all: PS> printenv `printenv` isn't a valid command. Did you mean: → Get-ChildItem env: (list all environment variables) Run it? [Enter = yes / n = no / e = edit] The case that act