The Register
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1 week ago
Developer Tarun Gupta created Autopilot-Jobhunt, a free AI tool that scans the web for job openings matching a user's profile and generates tailored resumes and cover letters. The tool uses free models like Llama through TinyFish's AI web agent and OpenRouter, with options to substitute Anthropic's Claude, and deliberately avoids automatically applying on users' behalf. Software developer job openings increased 15 percent since February 2025 while all other job categories declined 7 percent over the same period, suggesting growing demand for the tool in that sector.
Deep Learning Weekly
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1 week ago
This issue of Deep Learning Weekly covers recent AI model releases including xAI's Grok 4.5, OpenAI's GPT-Live voice model, and Mistral's open-sourced Leanstral 1.5, alongside developments in agentic AI systems and data infrastructure. Grok 4.5 achieves 80 tokens per second throughput and costs $2/$6 per million input/output tokens, while Leanstral 1.5 solves 587 out of 672 problems on PutnamBench. The newsletter highlights shifts toward agentic workloads requiring redesigned data systems, improved model interpretability through structural reasoning benchmarks, and engineering practices for optimizing AI agent efficiency and cost.
Amazon Science
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1 week ago
Anthropic released Turnstile, a proxy tool written in Rust that captures exact token-level data during reinforcement learning training of language models on multi-step tasks. Turnstile records token IDs, log probabilities, and loss masks at the moment of generation without modifying existing agent harnesses, solving the problem that transcript-based data loses critical information needed for effective RL training. The system enables RL training runs with existing agent harnesses as black boxes while handling complexities like mixture-of-experts routing and multimodal inputs from vision-language models.
TLDR
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1 week ago
Engineers are responsible for maintaining oversight of AI systems across multiple control loops, with humans required to validate and justify agent actions. The four critical loops requiring human involvement are the constraints loop, sampling loop, audit loop, and ownership loop. This distributed accountability model ensures that AI systems remain subject to human review rather than operating autonomously without justification mechanisms.
