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2026-03-20

• 10:11 UTC Ofcom don’t consider geoblocking the UK to be sufficient for an overseas website – Ofcom don't consider geoblocking the UK to be sufficient for an overseas website r/LegalAdviceUK: "I run a self-help forum for people with depression. Ofcom has been bombarding me with emails demanding I start ID-verifying and age gating my website": I started getting email from Ofcom [regarding OSA compliance] around November 2025 and now have multiple letters. I've repeatedly told them I'm from Canada, I'm not based in the UK. Eventually, I blocked all UK IP addresses in mid-February 2026 and told them I'd blocked the UK and that I was done engaging with them. I've now got ANOTHER email from them saying they're going to commence enforcement action against me because simply blocking UK IPs is "insufficient to comply with the Online Safety Act 2023." Tags: osa uk
• 10:07 UTC funny Waymo anecdote – funny Waymo anecdote on HN -- "Waymo saved my life in LA": When I visited LA, I rode in a Waymo going the speed limit in the right lane on a very busy street. The Waymo approached an intersection where it had the right of way, when suddenly a car ignored its stop sign and drove into the road. In less than a second, the Waymo moved into the left lane and kept going. I didn't even realize what was happening until after it was over. Most human drivers would've t-boned the car at 50+ km/h. Maybe they would've braked and reduced the impact, which would be the right move. A human swerving probably would've overshot into oncoming traffic. Only a robot could've safely swerved into another lane and avoid the crash entirely. Unfortunately, the Waymo only supported Spotify and did not work with my YouTube Music subscription, so I was listening to an advertisement at the time of my near-death experience. 4.5 stars overall. Tags: waymo funny anecdotes safety driving ai roads spotify via:hn

2026-03-19

• 18:16 UTC Measuring Agents in Production – Measuring Agents in Production "This 2025 December paper, "Measuring Agents in Production", cuts through the reality behind the hype. It surveys 306 practitioners and conducts 20 in-depth case studies across 26 domains to document what is actually running in live environments. The reality is far more basic, constrained, and human-dependent than TPOT suggest." This very much meshes with what I've seen and heard in real world usage. Lots of constrained LLM usage, carefully prompted, and reliability (consistent correct behavior over time) remains the primary bottleneck and challenge. (via Murat Demirbas) Tags: llm usage real-world ai agents papers via:muratbuffalo
• 09:50 UTC On the Biology of a Large Language Model – On the Biology of a Large Language Model Interesting research from Anthropic: The black-box nature of [LLMs] is increasingly unsatisfactory as they advance in intelligence and are deployed in a growing number of applications. Our goal is to reverse engineer how these models work on the inside, so we may better understand them and assess their fitness for purpose. [...] In recent years, many research groups have made exciting progress on tools for probing the insides of language models. These methods have uncovered representations of interpretable concepts – “features” – embedded within models’ internal activity. Just as cells form the building blocks of biological systems, we hypothesize that features form the basic units of computation inside models. However, identifying these building blocks is not sufficient to understand the model; we need to know how they interact. In our companion paper, Circuit Tracing: Revealing Computational Graphs in Language Models, we build on recent work (e.g. ) to introduce a new set of tools for identifying features and mapping connections between them – analogous to neuroscientists producing a “wiring diagram” of the brain. We rely heavily on a tool we call attribution graphs, which allow us to partially trace the chain of intermediate steps that a model uses to transform a specific input prompt into an output response. Attribution graphs generate hypotheses about the mechanisms used by the model, which we test and refine through follow-up perturbation experiments. Tags: claude llm research llms ai anthropic papers tracing

2026-03-18

• 16:33 UTC 2 Ways to Correct the Financial Times at AWS (So Far) – Last Week in AWS Blog – 2 Ways to Correct the Financial Times at AWS (So Far) - Last Week in AWS Blog This from Corey Quinn, on Amazon's recent AI-related production outages, is very good: A healthy engineering culture, when confronted with "your AI tool contributed to a production incident," responds with: "Yeah, that tracks. Here's what we're changing so it doesn't happen again." An unhealthy one responds with a condescending press release explaining why the journalist is wrong and probably an idiot, and the human is at fault. The engineers building and operating these systems are talented people doing hard work under increasingly constrained conditions. They deserve leadership that backs them up when things go sideways, not leadership that throws them under the bus to protect a product launch narrative. Tags: incidents production ai llms amazon aws communications pr
• 13:00 UTC Former Uber self-driving chief crashes his Tesla on FSD – Former Uber self-driving chief crashes his Tesla on FSD This is actually a really good article about Tesla, "full self-driving" (FSD), supervision, automation, risk and liability: Tesla is asking humans to supervise a system that is specifically designed to make supervision feel pointless. As he puts it, an unreliable machine keeps you alert, and a perfect machine needs no oversight, but one that works almost perfectly creates a trap where drivers trust it just enough to stop paying attention. The research backs this up. Psychologists call it the “vigilance decrement”, monitoring a nearly perfect system is boring, boredom leads to mind-wandering, and drivers need 5 to 8 seconds to mentally reengage after an automated system hands control back. But emergencies unfold faster than that. Krikorian cites an Insurance Institute for Highway Safety study showing that after just one month of using adaptive cruise control, drivers were more than six times as likely to look at their phones. Tesla’s own website warns FSD users not to become complacent, but the system’s smooth performance actively trains that complacency. He points to two well-known crashes to illustrate the impossible math. In the 2018 Mountain View accident that killed Apple engineer Walter Huang, the driver had six seconds before his Tesla steered into a concrete median. He never touched the wheel. In the 2018 Uber crash in Tempe, Arizona, sensors detected a pedestrian with 5.6 seconds of warning, but the safety driver looked up with less than a second remaining. In Krikorian’s own case, he did take action, but he was asked to snap from passenger back to pilot in a fraction of a second, overriding months of conditioning. The logs show he turned the wheel. They don’t show the impossible math of that transition. The pattern Krikorian describes should sound familiar to anyone who has followed Tesla’s FSD controversies: condition the driver to rely on the system, erode their vigilance through months of smooth performance, then point to the terms of service and blame them when something breaks. When FSD works, Tesla gets credit. When it doesn’t, the driver gets blamed. Tags: fsd tesla risk attention supervision liability driving safety vigilance automation
• 10:51 UTC Research highlight: Cliopatra: Extracting Private Information from LLM Insights – Research highlight: Cliopatra: Extracting Private Information from LLM Insights Research highlight: Cliopatra: Extracting Private Information from LLM Insights: When Anthropic came up with a new "privacy-preserving analysis system" to gain insights into AI use, and didn't use any provably robust notion to back up their privacy claims, I was mildly surprised. Surely they have both the money and the scientific maturity level to do better? But Clio, the system in question, sounded relatively reasonable, with multiple layers of risk mitigation built-in. Maybe adding differential privacy would have been overkill. I also didn't want to publicly criticize their approach in the absence of demonstrated real-world risk. So I didn't comment on their approach. You can probably guess where this is going. Fast forward to last week, and a new paper: Cliopatra: Extracting Private Information from LLM Insights, by Meenatchi Sundaram Muthu Selva Annamalai, Emiliano De Cristofaro, and Peter Kairouz. The authors show that with carefully designed attacks on Clio, they can bypass all the ad hoc mitigations, and successfully extract users' medical histories (1), in a way that provides 100% attacker certainty for some records. This is a new and clever take on an old attack. We've known for decades that k-anonymity is vulnerable to active attacks. Here, this is combined with prompt injection to encourage the LLM "summarizer" to actually include information from unique records. Perhaps more surprisingly, the authors find that some defensive layers are simply ineffective: the "LLM auditors" systematically report low privacy risk, and entirely fail to detect the attacks. Tags: privacy differential-privacy anonymity data-protection claude llms cliopatra infosec leakage

2026-03-11

• 13:22 UTC “nothing up my sleeve” numbers – "nothing up my sleeve" numbers This is great: "@jnsq.org: There's a concept in cryptography called a "nothing up my sleeve" number. Sometimes it's just the smallest number with the required properties. Sometimes it's pi or e or phi." Tags: numbers crypto cryptography maths
• 12:01 UTC Whole Brain Emulation Achieved: Scientists Run a Fruit Fly Brain in Simulation – Whole Brain Emulation Achieved: Scientists Run a Fruit Fly Brain in Simulation bloody hell this is amazing. As Charlie Stross noted: They've mapped the neural connectome of Drosophila and simulated it in silico. The experimenters went on to hook up their Drosophila connectome to an anatomically detailed Drosophila body model within an open-source physics engine that "uses generalized coordinates and constraint-based contact dynamics to simulate rigid-body systems with high fidelity" including joint and antennae modeling and accurate modeling of surface adhesion—and compound eye simulation. They managed to run a feedback loop between the full 127,400 neuron network in the biological connectome to the physical simulation, with feedback from proprioceptive signals received by the model "fly" in the simulation producing feedback spile trains in the simulation, and THEY GOT RESULTS: The behavioral repertoire observed in the demonstration included coordinated hexapod locomotion with both tripod and metachronal walking gaits, spontaneous postural correction in response to perturbation, initiation and execution of full antennal grooming sequences with the tripartite synchronization described by Özdil et al., and natural transitions between walking and stationary states. Every behavior arose from the same running brain model - there was no switching between different neural circuits or controllers. This is precisely what happens in a living fly: walking, grooming, and balance are different motor programs that coexist in the same brain and are selected and executed by the same biological circuits depending on the moment-to-moment state of the animal and its environment. Absolutely mind blowing -- a reconstructed, biological brain running in silico. Tags: simulation brains uploading drosophila flies emulation science biology neurons

2026-03-05

• 10:42 UTC Your binary is no longer safe: Decompilation – Your binary is no longer safe: Decompilation Brute-force decompilation and re-engineering of a binary (compiled) program, using Claude. The author takes an ancient MUD binary for BBSes, running as a Win32 DLL, and uses Claude, Ghidra, and the Ghidra MCP to first decompile the DLL to pseudo-C code with ~meaningful naming; then (and this is the really cool bit) uses a Claude-engineered scaffold to run the DLL in qemu with emulated inputs and outputs, so that property testing and differential testing approaches can be used to achieve decent code coverage of the re-engineered Rust implementation. This is really impressive. Deterministic simulation of the environment for the original binary is the key bit! Tags: claude decompilation reverse-engineering binaries software-archaeology qemu rust differential-testing fuzzing property-testing quickcheck

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