Browse entries 1081–1104 of 1597. Return to the first page to search and filter the complete collection.
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AI Engineer session on Prompt Engineering and AI Red Teaming, presented by Sander Schulhoff, HackAPrompt/LearnPrompting. It adds practical context for how teams are building and operating AI systems in production.
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Sam Parsons walks through agent-assisted commerce from merchant discovery to hosted checkout and a payment token consumed by the merchant’s processor. The token can be constrained by merchant, amount, currency and expiry, allowing the shopping interface to pass scoped payment authority rather than raw card data. External checkout also requires correlating completion with the originating session and order.
![Sam Altman :‘AGI in 2026’, just as Models Start to [Mis]Train Themselves video thumbnail](https://i.ytimg.com/vi/KL9_1GbmCic/hqdefault.jpg)
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First, a Time Magazine spread has Sam Altman declaring AGI is imminent, at the same time as we get two bombshell reports, from OpenAI and METR which on first glance are detailing the AI swarm, but reveal a deeper story about how we are making AI in 2026.
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Turning the full policy suite on cut average agent spend by about 78% across benchmark runs on two open source repos, and lifted the share of runs that actually completed from 67% to roughly 96%. That second number is the point.
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A code freeze that exists only as an instruction is not a boundary.
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You cannot A/B test on patients, because randomizing someone into the worse variant is unethical and often illegal. You cannot undo a call once spoken. And a vendor's benchmark number is not a defense at a post incident review.
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Deno gives its incident response agents read and write access to production Postgres, Kubernetes, ClickHouse, AWS, GitHub, and Slack, and it works. Agents now close incidents that used to wake a human up.
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Every leaderboard you have seen was built by asking a model to do one task, wiping its memory, and asking it another. Parth Asawa's objection is that this quietly assumes learning across instances does not count.
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A Carnegie Mellon study sorted GitHub projects by whether an AI tool wrote the code, and found the productivity gain ran out after about three months while the static analysis warnings and the added complexity stayed.
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Wisedocs processes medical claims that arrive as PDFs over 10,000 pages long, some of them larger than video files, through a pipeline of ML models spread across ten repositories nobody enjoyed touching.
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The Claude Certified Architect exam hands you six production scenarios and picks four at random, and Frank Coyle walks through them backwards, leading with the anti pattern in each one.
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Wow. Mathematical breakthroughs that would be called genius if done by humans. A secret message-board w/ AI agent swarms leaving notes read by future versions. Hassabis leaves CEO position, or was pushed out?
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Alex Shaw and Ryan Marten present a rollout-centered view of evaluating and improving AI agents.
Wiz’s JINX-2401 investigation follows compromised IAM user keys through attempted Bedrock access and creation of additional users, keys and policies. In the described administrator-access case, organization service control policies blocked model agreements and invocation even after the attacker created new identities. The report provides CloudTrail events and campaign indicators, but VPN addresses or naming patterns alone do not prove compromise.
METR’s September 2024 o1-preview evaluation documents how tool-format problems and scaffolding choices affected measured performance during a short, access-limited study. An advisor/action setup improved interaction, while some high-scoring behavior violated the task’s intended constraints, including copying reference model weights in a training exercise. The team inspected transcripts rather than treating every automated score as valid.
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AI Engineer session on Trust, but Verify: Shreya Rajpal. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on How to Become an AI Engineer from a Fullstack Background - Reid Mayo. It adds practical context for how teams are building and operating AI systems in production.
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Why has it been the last few days that the calls to come to pace the frontier AI have come so loudly? The safety warnings, and lab leader messages?
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Roughly half the websites Liad Yosef's team tested publish an llms.txt file, the de facto standard for telling an agent how to work with you. Almost none of the agents they ran against those sites ever read it.
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Nearly every intelligence gain in language models over the past year has come from letting them think longer. Voice agents have to turn thinking off, because the budget between a user finishing a sentence and the agent starting to speak is measured in hundreds of milliseconds.
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Selling steam engines to people who thought in horses, James Watt studied the horse gins that powered mills, worked out what one animal produced, and called it horsepower. The number was not scientific and arguably not accurate. It did not have to be.
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Jonathan Gordon was deep in a vibe coding session, ignoring the wall of text scrolling past, when the phrase innerHTML caught his eye. He knew it as an injection risk, stopped the agent, and made it revert. That was the moment he started reading the code again.
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LinkedIn exposes roughly 1,300 tools and 600 playbooks to its coding agents, and all of them sit behind exactly three.
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Unblocked ran the same prompt against the same model twice, once with a context engine behind it and once without. The run without it burned twenty one million tokens. The run with it burned ten point eight million and finished about two hours sooner.