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2026-04-01

• 10:13 UTC OkCupid gave 3 million dating-app photos to facial recognition firm, FTC says – OkCupid gave 3 million dating-app photos to facial recognition firm, FTC says This is a staggering breach of privacy. At this stage one has to assume that any data uploaded to a US company will be shared with whichever scumbag pays them the most. OkCupid and its owner Match Group reached a settlement with the Trump administration for not telling dating-app customers that nearly 3 million user photos were shared with [Clarifai], an [AI] company making a facial recognition system. OkCupid also gave the facial recognition firm access to user location information and other details without customers’ consent, the Federal Trade Commission said. Tags: us-politics data-protection privacy dating-apps okcupid match.com clarifai ftc

2026-03-27

• 12:35 UTC Why So Many Control Rooms Were Seafoam Green – Why So Many Control Rooms Were Seafoam Green Turns out it's US standard Industrial Color Coding, thanks to "color theorist" Faber Birren: With the increase in wartime production in the US during WWII, Birren and DuPont created a master color safety code for the industrial plant industry, with the aim of reducing accidents and increasing efficiency within plants. These color codes were approved by the National Safety Council in 1944 and are now internationally recognized, having been mandatory practice since 1948. The color coding went as such: Fire Red: All fire protection, emergency stop buttons, and flammable liquids should be red Solar Yellow: Signifies caution and physical hazards such as falling Alert Orange: Hazardous parts of machinery Safety Green: Indicates safety features such as first-aid equipment, emergency exits, and eyewash stations. Caution Blue: Non-safety information, notices, or out-of-order signage Light Green: Used on walls to reduce visual fatigue Tags: green design history color-theory faber-birren control-rooms industrial-design color-coding
• 12:32 UTC russellromney/turbolite – russellromney/turbolite I like this: "a SQLite VFS in Rust that serves point lookups and joins directly from S3 with sub-250ms cold latency": It also offers page-level compression (zstd) and encryption (AES-256) for efficiency and security at rests, which can be used separately from S3. Object storage is getting fast. S3 Express One Zone delivers single-digit millisecond GETs and Tigris is also extremely fast. The gap between local disk and cloud storage is shrinking, and turbolite exploits that. The design and name are inspired by turbopuffer's approach of ruthlessly architecting around cloud storage constraints. The project's initial goal was to beat Neon's 500ms+ cold starts. Goal achieved. If you have one database per server, use a volume. turbolite explores how to have hundreds or thousands of databases (one per tenant, one per workspace, one per device), don't want a volume for each one, and you're okay with a single write source. Tags: sqlite sql s3 aws gcp object-stores rust databases

2026-03-25

• 16:30 UTC TurboQuant: Redefining AI efficiency with extreme compression – TurboQuant: Redefining AI efficiency with extreme compression "TurboQuant is a compression method that achieves a high reduction in model size with zero accuracy loss, making it ideal for supporting both key-value (KV) cache compression and vector search. It accomplishes this via two key steps:": High-quality compression (the PolarQuant method): TurboQuant starts by randomly rotating the data vectors. This clever step simplifies the data's geometry, making it easy to apply a standard, high-quality quantizer (a tool that maps a large set of continuous values, like precise decimals, to a smaller, discrete set of symbols or numbers, like integers: examples include audio quantization and jpeg compression) to each part of the vector individually. This first stage uses most of the compression power (the majority of the bits) to capture the main concept and strength of the original vector. Eliminating hidden errors: TurboQuant uses a small, residual amount of compression power (just 1 bit) to apply the QJL algorithm to the tiny amount of error left over from the first stage. The QJL stage acts as a mathematical error-checker that eliminates bias, leading to a more accurate attention score. QJL: The zero-overhead, 1-bit trick QJL uses a mathematical technique called the Johnson-Lindenstrauss Transform to shrink complex, high-dimensional data while preserving the essential distances and relationships between data points. It reduces each resulting vector number to a single sign bit (+1 or -1). This algorithm essentially creates a high-speed shorthand that requires zero memory overhead. To maintain accuracy, QJL uses a special estimator that strategically balances a high-precision query with the low-precision, simplified data. This allows the model to accurately calculate the attention score (the process used to decide which parts of its input are important and which parts can be safely ignored). PolarQuant: A new “angle” on compression PolarQuant addresses the memory overhead problem using a completely different approach. Instead of looking at a memory vector using standard coordinates (i.e., X, Y, Z) that indicate the distance along each axis, PolarQuant converts the vector into polar coordinates using a Cartesian coordinate system. This is comparable to replacing "Go 3 blocks East, 4 blocks North" with "Go 5 blocks total at a 37-degree angle”. This results in two pieces of information: the radius, which signifies how strong the core data is, and the angle indicating the data’s direction or meaning). Because the pattern of the angles is known and highly concentrated, the model no longer needs to perform the expensive data normalization step because it maps data onto a fixed, predictable "circular" grid where the boundaries are already known, rather than a "square" grid where the boundaries change constantly. This allows PolarQuant to eliminate the memory overhead that traditional methods must carry. Tags: ai tech vectors search quantization turboquant research algorithms compression papers qjl error-detection polarquant
• 10:01 UTC Debunking zswap and zram myths – Debunking zswap and zram myths This is pretty compelling. I like this example: We have some concrete numbers to show this in practice. On Instagram, which runs on Django and is largely memory bound, we ran a test where we moved from their existing setup (with swap entirely disabled) to a setup with disk swap and zswap tiering. Django workers accumulate significant cold heap state over their lifetime, like forked processes with duplicated memory, growing request caches, Python object overhead, you get the idea. The results were twofold: We achieved roughly 5:1 compression. That's a huge benefit for such a memory bound workload, and also enables us to consider further stacking workloads. Enabling zswap reduced disk writes by up to 25% compared to having no swap at all(!). As you can imagine, as a result of this test, Instagram has been using zswap for many years now. Tags: kernel compression memory linux ops performance swap zswap zram

2026-03-23

• 17:31 UTC hectorvent/floci – hectorvent/floci "A free, open-source local AWS emulator. No account. No feature gates. No CI restrictions. Just docker compose up." Tags: floci aws emulation testing local coding
• 11:12 UTC GitHub – mautrix/whatsapp: A Matrix-WhatsApp puppeting bridge – GitHub - mautrix/whatsapp: A Matrix-WhatsApp puppeting bridge I've been investigating how I can back up my WhatsApp chat history and make it searchable (since WhatsApp's own built in search is not great). Turns out you can bridge WhatsApp into Matrix, and gateway your chats over to a self-hosted Matrix.org server. https://github.com/osteele/matrix-archive may then be a viable way to export those into a searchable format... or possibly this one? https://github.com/cameronaaron/matrix-archive/tree/master Tags: matrix whatsapp messaging chat interop searching self-hosted

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

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