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Founding Editor
Aditya covers the whole AI surface area for Signal — frontier models, agent infrastructure, the economics of inference, and the policy decisions that quietly shape what everyone else can build. He writes for operators who need a calibrated view of what's actually shipping versus what's keynote theatre.
The five vector stores worth considering in 2026 — what each costs, how fast it is, and the one question that decides most of them: do you even need a dedicated database, or is Postgres enough?
Sonnet 4.7 shipped this morning with Python execution baked directly into the API — no separate sandbox, no harness wiring, one new boolean parameter. Same model pricing as 4.6 plus a per-execution surcharge.
Signal's pricing calculator shipped without DeepSeek, Qwen, Llama, or Mistral. A reader called it out. Here's what changed, what the corrected pricing actually shows, and when each non-US model is the right pick.
Your dashboard says 90% cache hits. Your bill says otherwise. The gap is almost always three specific patterns inside the system prompt that don't show up in any log — and you can fix them in an afternoon.
The Model Context Protocol went from a quiet Anthropic release to industry-standard plumbing in under two years. Here's what it is, why every major lab adopted it, and where the hype outran the facts.
A from-scratch explainer of what embeddings actually are, how they're compared, why they make modern search possible, and which model to pick in 2026 — in about 15 minutes.
Most cache audits never happen because nobody owns them. Here's the version that survives — 5 minutes a week, one CSV file, and a GitHub Action that runs it whether the owner remembers or not.
Seven AI agents tested on the same task suite — Manus, Devin, Operator, GPT Agent Mode, Gemini Workspace, Replit, Claude Computer Use. Two are genuinely useful today; the rest need a specific use case.
Six AI coding tools, one honest conclusion: there's no single winner, and the best developers pair two. Here's what each is actually for, what it costs, and which combination fits your workflow.
OpenAI filed confidentially for what could be the largest tech IPO ever — at a valuation near $1 trillion, while losing more than a dollar for every dollar it earns. Here's what's actually going on.
Google's Gemini 3.5, launched at I/O 2026, is built around a single idea: stop answering questions and start completing tasks. Here's what actually changed and why it matters.
Grok 4.3 isn't trying to top the leaderboards. It's trying to be the model you can afford to run at scale — and on that bet, it mostly delivers. Here's where it fits and where it doesn't.
A 70% versus 95% cache hit rate isn't a model difference — it's whether you treat the system prompt as immutable code. Standardize the prefix, push every dynamic value into the user turn, and the misses disappear.
OpenAI flipped agent mode on for all Plus and Team accounts this morning. The capability is real; the practical use cases are narrower than the demo suggests.
We tested seven AI writing tools on the same set of tasks. Three are great, three are fine, and one is genuinely worse than nothing. Here's how they stack up.
Opus 4.8 lists at $5/MTok in and $25 out — a third of the prior Opus generation — with cache hits at $0.50. Here's what that does to the real per-request math for production teams.
A from-scratch explainer of how large language models actually work — tokens, attention, the inference loop, and what to make of it all — in 12 minutes.
A four-prompt workflow that turns a 4-hour blog post into a 75-minute one — without the soulless AI tells. Real prompts, real before/after.