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Feature
AI Agents Transition from Demos to Daily Use in Software Development
An empty AI-generated weekly digest highlights the challenges of integrating AI into routine tasks.
Why it mattersAI agents are becoming integral to daily software development tasks, moving beyond demos into real-world applications. This shift challenges developers to ensure these agents are reliable and their outputs trustworthy, requiring careful oversight and verification mechanisms.
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Each morning: models, tools, research, and conversations distilled from across the web — with why they matter for builders.
Around the Web
Provable AI Inference Progress
Users currently have no independent way to confirm whether an AI provider served the requested model or quietly substituted a cheaper one. FlashProve addresses this by using zero-knowledge cryptography to prove that a p…
r/artificial
AI Automation Dilemmas
If an agent reads incoming order-change emails but someone still copies every result into the CRM, you haven’t saved that person much work. I’d let it update something routine, like a delivery date, when the order ID ma…
r/nocode
We're trying an AI-generated weekly digest of issue tracker activity. Someone still reviews the draft before it gets shared. This week was uneventful, so the draft came back completely empty. A couple of teammates revie…
r/AI_Agents
Take the document and forget about software for a minute. Write down exactly what a human does with it. For example: Open PDF. Find customer name. Find account number. Find invoice total. Find due date. Enter those four…
r/automation
Tools
在 dsh 里装上这个插件即可，无需登录、注册或填 API Key，就能使用包括 DeepSeek V4.1 Flash、Kimi K3 在内的前沿模型——完全免费，不限量。 All you do is install this plugin in dsh: no login, no sign-up, no API key — the frontier m…
github | stars 60,641
Qwen3.8-Flash-Next on any consumer hardware: one-click install for Windows / Linux. Strata inference engine, OpenAI/Anthropic API on localhost, optional image input.
github | stars 39,889
Build
Renting the Frontier, Owning the Core IP: How We Cut AI Forecast Costs from $40M to $11M
thoughtworks
Beyond the quick-win: Designing AI systems around critical human judgment
thoughtworks
Research
Large language model (LLM) agents are now applied to astronomical data analysis. They retrieve the literature, reduce observations, and draft conclusions. Under pressure to complete a task, such agents can fabricate res…
arxiv
Politically biased AI can shift users' opinions, yet it remains unclear whether model disclaimers protect against such persuasion. We report a preregistered experiment (N=1,586) examining how disclaimer design and user…
arxiv
Multimodal web agents have shown promising capabilities for interacting with websites through graphical user interfaces (GUIs), yet realistic web tasks often require long-horizon, multi-step planning that remains challe…
arxiv
Playbooks
We have added installable Microsoft Agent Framework builder skills for .NET and Python. They give coding agents Microsoft-maintained guidance for building and modifying Agent Framework applications, from choosing the ri…
devblogs.microsoft
Explore 3 principles for effective human-in-the-loop workflows and how human-review is a strategic imperative for production-ready agents.
langchain
Guidance on how to design more rigorous and useful evals for agents across a range of agent architectures and use cases in real-world deployment.
anthropic
News
Release: datasette-llm 0.1a10 Fixes a bug that affected my agent.datasette.io demo site, where the set of available LLM models was not correctly restricted to just those in the configuration. Tags: datasette , llm
simonwillison
Learn how AI testing differs from software testing and how golden sets, RAG and agent evaluation, and monitoring help build reliable production AI systems.
deepsense
This post explores how Indegene built a production-grade, LLM-powered Data Quality Agent in its NEXT Unified Data Platform on AWS. The agent generates context-aware data quality rules, validates them, and produces ready…
aws.amazon