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Prompt injection turns AI agents against their owners. Learn direct and indirect attacks, real exploits like EchoLeak and GitHub MCP, and the containment defenses that actually hold up.
Article URL: https://en.wikipedia.org/wiki/Blindsight_(Watts_novel) Comments URL: https://news.ycombinator.com/item?id=49955297 Points: 9 # Comments: 2
September was a busy month for India’s tech and startup ecosystem, with AI and deeptech dominating the conversation. Funding activity…
Your security team says traces can’t leave the building. Your finance team says the observability bill can’t grow with traffic. Your engineering team says they...
A model that answers yes/no questions instead of writing text came out 3.5× cheaper and 3.8× faster than our gpt-4o-mini judge on 1,000 real production turns, and agreed with it on 897 of them.
Comet’s Opik platform is now listed in Nebius AI Cloud Applications, bringing agent observability, evaluation, and the full Comet AI development platform to one of the most powerful AI cloud platforms available today.
We ran a small model over thousands of Claude Code session and discovered how people really use coding agents. Comet Cost Intelligence is a tool for optimizing engineering token spend on Claude Code and Codex.
Despite how often prompts change once an agent is live, most teams don’t have a real prompt management process. Somewhere on a shared drive is prompt_final_v2.txt, and somewhere else is a version someone pasted into a Slack DM that is two edits newer.
When an LLM call is slow, expensive, or wrong, you want to see the whole request: the prompt, the model, the tokens, the tool calls, the latency. If your traces live in one tool and your evaluation lives in another, that picture is split in two.
The way developers interact with products is changing, and opening your app’s dashboard is no longer the obvious entry point. They want to use their apps inside the terminal. Why? Because they’re already there. We’re seeing more users instrumenting MCP and it makes sense.
Your new AI product is live. Infra dashboards are all green. Latency is low, error rates are flat, and CPU/GPU utilization looks healthy. Yet your Slack channels are full of screenshots from users asking, “Why did it do this?” The same user goal and high-level prompt can still produce wildly…
Someone on your team defaulted to the latest and greatest model available, which is also the most expensive model. Maybe it was an agent calling the flagship model for every tool call or a coding agent set to its most capable model out of the box. It worked, so nobody revisited it.
LLM applications are everywhere now, and they’re fundamentally different from traditional software. They’re non-deterministic. They hallucinate. They can fail in ways that are hard to predict or reproduce (and sometimes hilarious).
What Perplexity AI is in 2026: how its cited answer engine works, current pricing and models, how it compares to ChatGPT, and when to build your own instead.
Comet Model Guide 🔎 https://bit.ly/CometModel Comet Project Dashboard 🔎 https://bit.ly/cometProject Machine learning projects are a crucial aspect of learning ML, and most importantly they are a huge part of becoming a machine learning engineer.
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