Australia expose South Africa's batting frailties again as Scott Boland takes four wickets to leave hosts in trouble in second Test.

The court’s decision to review an admissions policy for selective middle school programs in Maryland follows its rejection of affirmative action in higher education.

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Weeks of student-led protests over conditions in French schools spiraled into violent protests across the country in recent days.

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The Florida Department of Highway Safety and Motor Vehicles (FLHSMV) has begun notifying victims impacted by a recent data breach of driver’s license and motor vehicle data, but not everyone whose information was stolen will receive an alert.

In a notice posted Friday, FLHSMV confirmed that it took “immediate steps” after discovering that “a malicious actor accessed certain department records on September 4, 2026, using compromised credentials.” There’s “no evidence” that data has yet been misused, the department said, but law enforcement “determined the information potentially affected includes driver license and motor vehicle data, to include document images, and other personal information.”

FLHSMV is sending out letters to everyone affected, offering victims one year of credit monitoring from Sontiq, Inc. However, the state acknowledged that it may not have current contact information for many former Florida residents’ whose data was still being stored. Those potential victims can confirm if their data was stolen by emailing FLHSMV at customerinquiry@flhsmv.gov, officials said.

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Article URL: https://po-ru.com/2026/10/18/why-im-no-longer-updating-this-repository

Comments URL: https://news.ycombinator.com/item?id=50154518

Points: 77

# Comments: 23

The apparent attack on MP Helen Haines suggests the worst fears of politicians and public figures appear to have been realised.

Find insight on Chinese automakers, DHL Group, Mullen Group and more in the latest Market Talks covering the Auto and Transport sector.

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Ronald Araujo has impressed since arriving at Liverpool from Barcelona. Could he prove a longer-term solution?

Find insight on Intel, Nokia and more in the latest Market Talks covering Technology, Media and Telecom.

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The Saudi government did not immediately respond to BBC requests for comment.

The Future Investment Initiative, a centerpiece of Saudi efforts to diversify its economy from its reliance on the energy industry, was set to begin next week.

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The Future Investment Initiative, a centerpiece of Saudi efforts to diversify its economy from its reliance on the energy industry, was set to begin next week.

© Fayez Nureldine/Agence France-Presse — Getty Images

Obama says federal agents' search of the Democrat's home was an effort to intimidate a Trump critic.

The longtime ally behind the scenes of Keisha Lance Bottoms’ career

Paul Nthenge Mackenzie told his doomsday followers in a remote forest to fast for salvation. He faced trial after hundreds of bodies were exhumed.

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Apple has sent invites to a second product launch this month, and it's scheduled for October 28th at 1PM ET in Los Angeles.

At the event, Apple is set to announce new Macs and a refreshed iPad Mini, according to Bloomberg. The big announcement is expected to be a new MacBook Pro with an OLED touchscreen that's also lighter and has an iPhone-like Dynamic Island. But Apple is also planning on revealing a 14-inch MacBook Pro and a new iMac with the M6 chip, which was recently introduced with the Mac Mini. Apple's updated iPad Mini will apparently finally get an OLED screen as well and put the selfie cam on the landscape edge.

"Can't wait to p …

Read the full story at The Verge.

Hi HN, we’re Robert and Din from Laminar (https://laminar.sh). We’ve been building open-source observability for AI agents since 2024. Recently, we released flow-1, a model trained with RL to find errors in agent traces. On our benchmark (https://laminar.sh/blog/flow-1), it matches GPT-6-sol in detection quality while being 23x cheaper. This makes it affordable to analyze every production agent trace, without sampling.

Here’s a one-minute walkthrough: https://youtu.be/KJa3h6ZJNLQ

We also put together a post with example traces and analyses from flow-1 and other models: https://laminar.sh/blog/flow-1-in-action

Agent observability is crowded, so instead of a bullet list of how Laminar differs from X, we’ll explain how we’re solving agent observability from first principles.

Agents now run for many steps and fail implicitly. An agent can click the wrong button or cite the wrong document while every call completes successfully. These errors are the most useful ones to catch and understand. To find them, you need to understand what the agent was trying to do and inspect its LLM calls and tool results. Simply looking at the final agent output is not enough.

Collecting traces alone doesn’t solve this. A lot of agent observability platforms are still data collectors at their core. You can end up paying to store traces that nobody has time to read or extract value from.

Using another LLM to inspect traces is THE solution, but capable models are expensive at scale and cheaper ones often miss errors or flag unrelated things.

So we built our model to solve exactly this problem. We finetuned a small OSS model and then post trained it with RL on thousands of synthetic investigations of realistic agent workflows in software, legal, customer support, etc. We got a model that is both good at agent trace analysis and very cheap to run.

flow-1 runs inside a harness we call Signals. It acts as a coding agent, treating an agent trace like a repo, with each span (an LLM call, tool call, etc.) as a file. It starts from a compressed string representation of a full agent trace and a user prompt, then greps across spans and reads the relevant ones in full.

The beauty of Signals is that it can respond to a general prompt. You can ask it to identify user frustration, check whether your browser agent completed a checkout, and so on. It’s not limited to raw error detection. It returns findings as JSON in a schema users can freely define. We call these signal events.

Once you’re analyzing every trace, you also have too many individual events to read. That’s why we automatically group similar signal events into clusters. You can see recurring problems and how often they happen, then drill into individual events and the traces behind them.

You can use traces from a cluster to build an eval dataset for that type of failure. You can then use your favourite coding agent to hillclimb on those evals and check that the fix works across these cases before shipping it to prod.

Trace → analyze → catch errors and form clusters → fix → release. Making this loop easy is why we built Laminar.

We’ve put the same thought into the rest of Laminar. Our blog (https://laminar.sh/blog) explains how looking closely at how agents actually work led us to build 20x trace compression, a custom SQL query engine and automatic task extraction.

Laminar is Apache 2.0 and self-hostable (https://github.com/lmnr-ai/lmnr). We make money from the managed cloud.

We’d love to hear how you track your agents’ performance today. How much do you learn from user feedback versus your own monitoring? And when you fix something, how do you check that it hasn’t introduced a different failure?

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Comments URL: https://news.ycombinator.com/item?id=50154029

Points: 15

# Comments: 3

Trees are suffering one devastating epidemic after another, unleashed by insects and bacteria that enter the country because of global trade.

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Article URL: https://www.githubstatus.com/incidents/8tzbpcnl75jp

Comments URL: https://news.ycombinator.com/item?id=50153934

Points: 87

# Comments: 62

The fire, which did not seriously damage the network, came after similar incidents in Bulgaria and Poland involving the satellite internet provider, a key defense tool for Ukraine’s armed forces.

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