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There is a Princeton paper by Kapoor and Narayanan. They found data leakage in close to 300 papers across 17 fields, including medicine and economics. Leakage means the model was trained on information it would never have when it makes a real prediction. So it looks great on the test set and then fails in the real world. My favorite example is civil war prediction. Complex models were reported to crush old logistic regression. Once the leakage was fixed, the fancy models were no better than the decades old stats. I have built enough models to know how easy this is to do by accident. You scale the data before you split it, or you use one feature that is really a stand in for the answer, and your numbers look amazing. So now when I read another "AI cracked X" headline, my first thought is whether anyone checked it for leakage. submitted by /u/kamilc86 [link] [留言]

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built I started this project as a way to escape the noise of social media we have today and to focus on what matters. It was a personal project at first and I just wanted to get an uncluttered messaging working. After a while a few friends of mine started looking into it and liked the vision as well. Then I received a notification of the event and that excitement of finishing it up got all over me - I felt like a kid again. Demo Because I want to have control over my data, I self host the version on a small microcontroller, which is more than sufficient for me and my friends. To get a sneak peak at how it looks, check out this . (Be aware that this is not functional as there is no server connected) I made some demo accounts to give a sense of how the app looks and feels: The Comeback Story Because I had the messaging already working, I had to mainly implement the rest of the features, including enhanced profiles, networks and posts creation. Another thing I really had to work on was the design. Before, it was mainly a black and white testing ground - something every end user would be scared of. My Experience with GitHub Copilot I am really not talented in terms of designing a usable interface. However, my friends really didn't want to use a half-baked up command line application. Copilot really helped me achieve that polished look. Just to give a glimpse of how it feels, one friend of mine even described it as an enhancing feature when navigating through the tabs. Furthermore, although it mostly was bug free, "It worked, until it didn't". Sometimes I spent hours fixing or rather finding some annoying bugs. And because Copilot is a real expert in these languages, it was quite a moment when it guided me towards finding the mistakes I made.

After using Claude Code, Codex, and Pi Agent for months, I wondered: how many AI agents are on my machine? I built a scanner. Here's what it found: Framework Active Archived Claude Code 192 191 Codex CLI 37 0 Pi Agent 8 sub-agents 12 scripts MCP Servers 8 - Total 448+ The Waste Duplicate calls : Same prompt → 3 agents (8-18% waste) Overqualified models : Simple tasks on expensive models (15-25% waste) Cache fragmentation : No shared prompt cache (12-20% waste) Zombie agents : Archived still indexed (2-8% waste) → 30-50% of AI API spend is wasted. The Fix AMA — Agent Management Agent bash pip install ama-core && ama scan && ama start Scans all agents across frameworks Smart routing (simple task → cheap model) Lifecycle management Local dashboard at localhost:8765 Free calculator: ama-agent-store.vercel.app/calculator MIT licensed. Feedback welcome!

submitted by /u/Bubbly-Air7302 [link] [留言]

Sony will deliver more than an hour of trailers and game news this week.

submitted by /u/techzexplore [link] [留言]

From CFD and FEA to digital twins, carmaking now involves a lot of virtualization.

The Instagram accounts for the Obama White House and the Chief Master Sergeant of the U.S. Space Force were briefly defaced with pro-Iranian images and messages over the weekend, after instructions began circulating on Telegram showing how to trick Meta's "AI support assistant" bot into resetting account passwords.

Hackers stole usernames, hashed passwords, and other data from a service that allowed players to cheat in Grand Theft Auto V.

The AI giant behind Claude submitted paperwork on Monday that would take it public, just a couple of weeks after SpaceX’s splashy IPO announcement.

Lawsuit seeks $12,000 from startup that allegedly damaged home in robot tests.

BYD is giving its self-driving tech a big vote of confidence.

The 5800X3D returns at $349, while the 7700X3D debuts at $329.

Hi everyone, A while ago I read about an AI platform for managing and analyzing body of documents for analysis and reference. From what I remember, it was a semi-closed system where you could upload your own source materials and the model could analyze and reference your uploaded documents directly. From what I recall, it wasn’t self-hosted. Does anyone know of a tool like this or recommend something that performs better than other models? Any recommendations, even if it's a different tool with similar capabilities, would be really helpful. Thanks in advance! submitted by /u/nero_rosso [link] [留言]

Anthropic introduced Dynamic Workflows, a new capability for Claude Code designed to handle complex software engineering tasks by coordinating large numbers of AI agents within a single workflow. The feature allows Claude to dynamically create orchestration scripts, break work into subtasks, run them in parallel, and validate results before presenting a final answer. By Robert Krzaczyński

New article: “ Responsible Disclosure in the Age of AI: A Call for Urgent Action ,” by Melissa Hathaway. Abstract: Artificial intelligence is fundamentally reshaping the balance between vulnerability discovery and remediation. Frontier AI models are now capable of autonomously identifying exploitable software vulnerabilities at unprecedented speed and scale. This development exposes decades of accumulated technical debt created by a software industry that prioritized rapid deployment over secure-by-design engineering practices. Drawing on the evolution of software assurance, vulnerability disclosure frameworks, and U.S. cyber policy, this perspective argues that the current moment represents a strategic inflection point for governments, industry, and critical infrastructure operators. The author examines the growing tension between offensive and defensive equities in cyberspace, the emergence of AI-enabled vulnerability discovery capabilities in both the U.S. and China, and the increasing risks posed by unsupported legacy systems and AI-assisted code generation practices. Responsible disclosure can no longer remain a reactive or fragmented process, but must become a coordinated national and international resilience effort involving governments, software vendors, infrastructure operators, and emergency response organizations. The article concludes with an urgent call for accelerated remediation, large-scale patch management coordination, and sustained investment in automated vulnerability repair capabilities before adversaries exploit this rapidly narrowing window of opportunity...

submitted by /u/Fcking_Chuck [link] [留言]

Mike Schroepfer's Gigascale Capital has raised a large fund to back founders building climate-friendly solutions for the world's energy and material shortages.

After months of speculation about whether OpenAI or Anthropic would be first in their race to IPO, Anthropic on Monday reached a key milestone: filing to kick off the process with the U.S. Securities and Exchange Commission. The filing sets the stage for what's sure to be a massive IPO. As of its fundraise last […]

The company said Monday it has filed confidentially for an IPO.