How I Fight AI Brain Fry
Article URL: https://sergeykuhne.com/blog/fighting-ai-brain-fry Comments URL: https://news.ycombinator.com/item?id=50416363 Points: 83 # Comments: 56
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Show HN: Fs-relay – Mount remote folders over HTTP 10x faster than s3fs (Go)
Article URL: https://jadmadi.net/posts/mounting-remote-directories-over-http/ Comments URL: https://news.ycombinator.com/item?id=50423261 Points: 1 # Comments: 0
Show HN: Record, branch, and compare command runs in stdlib-only Python
Hi, this is a package I developed for keeping track of short toy-model development cycles and explaining what changed between runs.The core idea: commands run through the package, and their declared parameters (or injected parameters with --substitute) and outcomes go into a dependency-free ledger stored in the project. Because that ledger lives in the project directory, it has worked well for passing context between different agents. I’ve also found some use for it in CI work. Comments URL: https://news.ycombinator.com/item?id=50417782 Points: 3 # Comments: 0
Show HN: AIDA – agentic generated user guides
Article URL: https://github.com/tryaida/aida Comments URL: https://news.ycombinator.com/item?id=50412923 Points: 2 # Comments: 1
How Greenlight Financial built an autonomous SOC solution on Amazon Bedrock AgentCore
Greenlight Financial resolves more than 90% of up to 4,000 monthly security and risk cases without human intervention using Luna, a tiered multi-agent system on Amazon Bedrock AgentCore. This post covers the architecture, episodic memory for continuous learning, and the expansion from SOC operations into financial risk triage.
Show HN: S3mail – Serverless personal email on AWS with JMAP
Article URL: https://github.com/zero11-0203/s3mail Comments URL: https://news.ycombinator.com/item?id=50401533 Points: 1 # Comments: 0
Taxonomy of agentic primitives in Amazon Quick
As we built Amazon Quick, its agents grew from answering questions to taking actions, and the same primitives kept getting rebuilt under different names. This post shares the taxonomy we converged on — triggers, agents, capabilities, and learned context — found by decomposing agentic work into four plain questions: When, What, Who, and How.
Show HN: Eski – an in-memory store in Odin, measured against Redis on one core
Article URL: https://github.com/vighnesh-bheed/eski Comments URL: https://news.ycombinator.com/item?id=50389748 Points: 1 # Comments: 0
How to Build Proactive, Always-On Agents with the Schedules SDK in Managed Deep Agents
Most proactive agents run on a simple cron. Learn what scheduled runs unlock, from memory to smart triage, and how to add them to Managed Deep Agents.
Nvidia releases all-in-one adaptable Nemotron 3.5 audio transcription and translation model
Nvidia Corp. today released a leaderboard-winning open artificial intelligence model for multilingual audio transcription and translation, along with an open data pipeline for fine-tuning Nemotron 3.5 Transcribe. Traditional transcription AI models follow a workflow that detects speech, transcribes it, identifies and labels different speakers, then answers questions about the transcription. Transcribe does all of this […] The post Nvidia releases all-in-one adaptable Nemotron 3.5 audio transcription and translation model appeared first on SiliconANGLE.
AI Emergency Alerts Are Coming. Humans Might Not Be Ready
Human beings struggle with emergency communication. Chatbots can help—if people take them seriously.
Announcing SOCI-indexed images for faster SageMaker AI jobs
Seekable OCI (SOCI) indexing for Amazon SageMaker AI training and processing jobs takes full container image download off the critical path. SageMaker AI lazy-loads your image to start containers in seconds instead of minutes, with no changes to your job definition. See the benchmarks and how to adopt it.
Groundhog Day for AI agents: Prompt caching patterns for using OpenAI models on Amazon Bedrock
Every time an agent takes a step, it re-reads its entire context before acting. Here is how prompt caching for OpenAI models on Amazon Bedrock lets your agent remember, cutting input cost and latency without resending the same tools, instructions, and history every turn.
Showing our work: a controlled experiment in context engineering
We ran one AI skill 80 times against four context sources. Engineered summaries cut cost 48.5% and run time 43.2%.
Why BigQuery can’t match ClickHouse Cloud for real-time analytics
ClickHouse Cloud delivered 438× better performance per dollar than BigQuery in CostBench. We trace the gap from fresh data arriving to fast answers coming back.