Kimi K2 thinking: The open-source model giving closed AI labs a run for their money
With performance that rivals the best proprietary models, Moonshot AI’s new open-weights release, Kimi K2 Thinking, signals a major power shift in the AI race. The post Kimi K2 thinking: The open-source model giving closed AI labs a run for their money first appeared on TechTalks .
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Long Term AI Memory by creator of Apache Cassandra
cortexdb.ai CortexDB is the long-term memory layer for AI systems — The problem is fundamental: today's AI agents are stateless. Every conversation starts from zero. The dominant approach to giving AI memory — having an LLM rewrite and merge your data on every single write — is lossy, fragile, and ruinously expensive. The LLM decides what to keep and what to throw away, replaces the original with a summary, and that decision is irreversible. Information it deemed unimportant today may be exactly what a future query needs tomorrow. CortexDB takes a fundamentally different approach: every piece of information is appended to an immutable event log and never overwritten. A lightweight LLM extracts entities and relationships asynchronously, but the original data is always preserved — if the ext

Prologue: After We No Longer Write Code by Hand, What Remains for Engineers?
1. A Question We Can No Longer Avoid See Figures 0-1 and 0-2 in this chapter. Over the past decade, software engineers have had a broadly stable understanding of themselves. We proved our value by writing implementations, reading systems, fixing bugs, refactoring, and aligning team collaboration. Even as job specialization became more detailed, that central image did not change: an engineer was, first of all, someone who personally built complex things. But once agents began to enter real development workflows, that image was quietly unsettled. Code implementation, test scaffolding, documentation patches, simple regressions, fault reproduction, and localized fixes—more and more steps that once depended on human hands began to be handed over to models. The change is uneven and far from comp

Agents Can Pay. That's Not the Problem.
On April 2, 2026, the x402 Foundation launched under the Linux Foundation. The founding members included Visa, Mastercard, American Express, Stripe, Coinbase, Cloudflare, Google, Microsoft, AWS, Adyen, Fiserv, Shopify, and a dozen others. Twenty-three organizations representing essentially the entire payments industry signed up on day one. The announcement celebrated something real: the agent payment problem is, for practical purposes, solved. Any AI agent on the planet can now send a payment to any resource that accepts x402. The plumbing is done. This is worth sitting with, because it changes the nature of the problem. If the question was "can agents pay?" — x402 answers it. If the question was "will the payment networks support this?" — 23 members of the Linux Foundation answer it. If t
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Agents Can Pay. That's Not the Problem.
On April 2, 2026, the x402 Foundation launched under the Linux Foundation. The founding members included Visa, Mastercard, American Express, Stripe, Coinbase, Cloudflare, Google, Microsoft, AWS, Adyen, Fiserv, Shopify, and a dozen others. Twenty-three organizations representing essentially the entire payments industry signed up on day one. The announcement celebrated something real: the agent payment problem is, for practical purposes, solved. Any AI agent on the planet can now send a payment to any resource that accepts x402. The plumbing is done. This is worth sitting with, because it changes the nature of the problem. If the question was "can agents pay?" — x402 answers it. If the question was "will the payment networks support this?" — 23 members of the Linux Foundation answer it. If t

I Built npm for AI Skills — Here's Why AI Needs a Package Manager
AI skills are stuck in copy-paste hell. spm fixes this — install reusable AI instructions with one command, works with Claude, Cursor, VS Code, and 11 more clients via MCP. Every developer knows this pain: you find a perfect AI prompt. Maybe it's a code review checklist that catches bugs your linter misses. Or a set of prompt engineering techniques that dramatically improve your LLM outputs. You save it somewhere. A note. A file. A Slack message to yourself. A week later you need it again and can't find it. So you rewrite it from scratch. Or worse — you find a version, but it's outdated, and you don't remember which copy is current. This is 2026 and we're still managing AI knowledge with copy-paste. The problem is obvious once you see it Software development solved this decades ago. Before



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