US lawmakers are alarmed that Bill Pulte, a housing official with no intelligence experience, is poised to take charge of one of the government's most powerful surveillance tools.

Turn photos into game assets Discussion | Link

Apple and Google are updating their smart home streaming devices to Thread 1.4. As first spotted by Matter Alpha and 9to5 Google, the latest spec has arrived on compatible Apple TVs in the tvOS 27 developer beta and the Google TV Streamer through a software update. This lays the groundwork for these devices, which serve […]

Electricity demand from AI data centers is pushing everyone — including automakers like GM and Ford — into the energy storage business.

Companies are burning through exorbitant sums of money to keep pace in the AI arms race. Debt is climbing.

The CFTC is considering its first regulation for prediction markets, as arrests over "insider trading" on everything from military operations to Google Search data continue to stack up. As CoinDesk reports, a notice of proposed rulemaking says "the proposal would establish a structured framework for evaluating whether such contracts involve an activity enumerated in Section […]

The never-ending problem of scammers has made Valve decide to stop producing Steam gift cards.

In these modern harrowing times, more and more posts are proving to be AI slop, and many people are averse to that. Do you think it would it be useful to have a way to indicate that you believe a post is slop separate from the existing flag button? Maybe it's just me but I feel like having a dedicated [AI slop] flag on a post would be useful because I feel differently about posts that have been flagged for other reasons than that. (or something friendlier if value is seen in the general idea but

The delayed Fable reboot is coming to Xbox Series X/S, PS5 and PC on February 23.

Access OpenAI models and Codex through Oracle Cloud, using existing commitments to build and deploy AI with enterprise security and governance.

Recently open-sourced by Microsoft, pg_durable is a PostgreSQL extension that enables durable workflows to run natively inside the database, eliminating the need for external orchestration systems. By Sergio De Simone

We played the opening prologue of the horror sequel, and it's still got ways to bring the scares.

The Mobi Fold is an $80 Bluetooth mouse with a silicone-wrapped hinge.

North Korean hackers posing as remote IT workers and recruiters remain a major threat to U.S., European, and Asian companies, accounting for about half of all attacks over the past 12 months.

One thing I've noticed while looking at production AI systems is that getting the first model deployed is rarely the hard part anymore. Most teams can build a AI apps like, support bot, document assistant, or agent workflow fairly quickly. The harder problem starts a few weeks later. Real users don't behave like benchmark datasets. They use internal terminology, ask incomplete questions, upload messy documents, and interact with systems in ways nobody anticipated during evaluation. As usage grows, you start seeing patterns: Certain questions consistently produce weak responses. New product terminology appears that wasn't in the original training data. Users find edge cases that never showed up during testing. The model performs well in some workflows and poorly in others. The problem is that most AI systems don't learn from any of this. Inference logs sit in one system. Training datasets live somewhere else. Fine-tuning pipelines live somewhere else. Evaluation is done using different tool. So every model improvement cycle becomes a project of its own. This is the biggest bottlenecks in production AI today. Not training but Model Iteration. Training is also a crucial part of it. Can you take production usage, identify failure patterns, turn them into datasets, improve the model, redeploy it, and repeat the process without rebuilding the entire workflow every time? The teams getting the most value from AI seem to be building feedback loops instead: production traffic → dataset curation → post-training → evaluation → redeployment Then repeating that cycle continuously. I recently tried the approach on one Insaurance chat usecase, and my pipeline kinda look like this: https://preview.redd.it/kdo9vytzfi6h1.png?width=1272&format=png&auto=webp&s=03d9799ace5a567eafd004a1d141084af6ee5afb I was looking at how platforms like Data Lab approach this problem recently, and the interesting part wasn't the fine-tuning itself. It was treating inference logs, datasets, post-training,