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Show HN: Getting GLM 5.2 running on my slow computer

A few days ago I found myself trying out GLM 5.2 and was really positively impressed. The capabilities and security I was getting from this LLM are similar to those I've gotten from models like Claude or GPT, and this really surprised me. But then I thought, "I wonder how it would work on a normal computer like mine," and above all, "I wonder if it would work without going into OOM on a computer like mine." So I started working with the help of agents to test this possibility. I started converti

2026-07-09 原文 →
开发者

Four nuclear reactors hit a big milestone in the US

I was really looking forward to July 4, and not just because I love a poolside barbecue. This year the American holiday also marked a big symbolic deadline for US nuclear power. Last year the Trump administration set a goal to see three new microreactors achieve criticality, a technical milestone establishing that a reactor can…

2026-07-09 原文 →
AI 资讯

Series Week 24/52 — Cloud Migration: Finding Your Path in the Database Migration Minefield

{ Abhilash Kumar Bhattaram : Follow on LinkedIn } The Post-Migration Mirage For many Chief Technology Officers (CTOs), the successful cutover of a core database to the cloud feels like the ultimate victory lap. The data has landed, the connection strings are updated, and initial performance metrics look stellar. But there is a dangerous mirage that follows a cloud database migration: Hidden Downtime. Unlike an abrupt database crash, hidden downtime is a slow-burn operational decay. It happens when day-to-day transactions process smoothly in production, but the underlying database ecosystem—specifically the disaster recovery (DR) standby instances, secondary cross-region sites, and replication pipelines—quietly falls out of sync. When a true disruption occurs and you try to failover or scale, the database tier collapses. To ensure true, 24/7 predictability, forward-thinking CTOs look beyond the immediate "Go-Live" date. The ultimate challenge is navigating the dense maze of cloud onboarding options to find the exact database migration method that fits your specific application topology. Ground Zero: The Database Configuration Drift The root cause of post-migration database downtime begins long before cutover day, it starts with how the database is moved and how its configuration is maintained. Going to the cloud offers various technical pathways, but the overarching challenge is finding what fits your unique architecture. The initial migration must establish perfect baseline parity, but standard database operations and hasty migration choices quickly introduce fatal configuration drift. To manage this drift effectively, organizations must introduce rigorous baseline metrics before, during, and after the migration process: - Benchmarking Versions: Ensuring that source and target database patch levels, Timezone (TZ) files, and Release Updates (RUs) match exactly. Mismatched database versions between primary cloud instances and standby homes create silent dictionary inc

2026-07-09 原文 →
AI 资讯

Anthropic Shipped @Claude For Slack. My Team Runs On

Anthropic Shipped @claude for Slack. My Team Runs on Telegram. Anthropic just shipped @Claude inside Slack channels. Tag the bot, it reads the thread, does work async, posts back. Nice product. Except roughly 95% of small businesses don't live in Slack — they run on WhatsApp, Telegram, and Gmail. If you're a solopreneur or a 1-to-10-person team, here's the exact four-part recipe I use to run the same pattern in Telegram for under $12/month. What Anthropic actually shipped (and who it's for) Anthropic shipped an enterprise distribution deal wearing a product launch t-shirt. @Claude for Slack lets you tag the bot in a channel or thread, gives it channel memory, connects to your other apps, and returns work asynchronously — but only on Slack Team and Enterprise plans. That's the punchline: it lives where the annual contracts live. Look at the raw user counts. Slack's own reporting puts it around 35–40 million weekly active users globally. WhatsApp is over 2 billion. Telegram is over 900 million. Gmail sits around 1.8 billion. In the 1-to-10-employee segment outside US tech, Slack penetration is single digits. Small teams in Europe, LATAM, and most of Asia coordinate in WhatsApp groups and run pipeline out of Gmail. They are not about to add Slack seats at $15/user/month just to get an @Claude mention. That's a rational call for Anthropic — Slack is where the enterprise procurement motion already exists. It's just not a product for the operator segment. And the pattern they productized is trivially replicable on any messenger with a bot API. Platform Weekly/monthly active users Bot API Cost to run a mention-bot Slack ~35–40M WAU Yes, paid plan $15/user/mo + API Telegram ~900M MAU Yes, free ~$5–12/mo API only WhatsApp Business ~2B MAU Yes, metered $0.005–0.08/conversation + API Gmail ~1.8B MAU Pub/Sub push Free tier + API The four-part recipe (works in any messenger) Every mention-bot is the same four moving parts: a webhook that fires on mention, a context store that ho

2026-07-09 原文 →
AI 资讯

Escrow with a judge vs atomic locks: where agent trades actually need each

In January, three researchers built a shopping agent on Google's Agent Payments Protocol (AP2), the standard designed to make agent-led purchases safe through cryptographically verifiable mandates. Then they attacked it with nothing more exotic than adversarial text. The paper, "Whispers of Wealth" ( arXiv 2601.22569 , revised May 2026), reports that simple prompt injections reliably subverted the agent: one attack steered which products the agent ranked and bought, another exfiltrated sensitive user data. The part of the stack that failed was not the cryptography. The mandates verified exactly what they were designed to verify. What folded was the layer that exercises judgment. Hold that result in mind, because the agent economy is currently pouring money into judgment. Everyone is hiring a referee Look at what shipped in the last few months for agent-to-agent commerce, and a single pattern repeats: put the money in escrow, and let a judge decide when it comes out. ERC-8183 formalizes it: funds sit in an escrow contract while an Evaluator - an agent or a human - decides whether the deliverable meets the spec before releasing payment. It is the pattern Virtuals' Agent Commerce Protocol runs on. Circle has piloted an escrow agent for USDC flows. Kustodia and Nava (which raised $8.3M) are startups built on the same shape. And on July 1, BNB Chain and AWS launched agents that bank themselves - agents deployed to Amazon Bedrock AgentCore with their own wallets, identity, and payment stack from birth. Even the category label is contested now: at least one project has declared itself an "MCP Settlement Standard" from a landing page. That is five separate, serious teams independently converging on the same component: a referee who holds the money. The referee exists for a good reason Before arguing against the judge, steelman him. Most agent-to-agent commerce today is hiring: one agent pays another for work. Write this code. Produce this research. Render this video. The de

2026-07-09 原文 →