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A few weeks ago I killed an indicator of mine in public. I had been trying to work out how much of my audience was automated. One signal was whether an account had uploaded its own avatar. It fired on 100% of the accounts I was confident were people and 97% of the ones I suspected were not. That isn't a lenient signal. It isn't separating anything — it tracks something both groups share, and I had been counting its votes for weeks. I wrote that up. Named the defect, retired the indicator, moved on feeling like I'd learned something. Three days later I shipped another one. The same hole, in a different shape I needed to check whether a comment on one of my posts was actually visible to readers — I'd found one the API returned and the comment count included, but that moderation had removed. So I wrote a check: // v1 — passes for anyone with a second comment on the page. Zero separation. visible : html . includes ( comment . user . username ) // v2 — the only witness with jurisdiction over one comment. visible : html . includes ( comment . id_code ) Two comments from the same account, one removed and one live, both came back visible under v1. I found it by accident, and only because I happened to compare against something else. Someone in a thread put the problem in a sentence I couldn't argue with: if the fix is "I noticed this one," the next indicator ships with the same blind spot in a different shape. Which is, word for word, what I had already written about the previous defect. Their prescription was structural. A labeled control set shouldn't be a diagnostic you run when something feels off. It should be a permanent seed every indicator has to clear a margin on before it's allowed to vote — not just beat chance on the live population, which is exactly the condition that let the avatar signal pass silently. Building it Twenty-eight accounts. Fourteen labeled human, fourteen automated, and every label carries a provenance string saying how it was established — seve

MacPaw is building a local version of its AI assistant Eney using Liquid AI's models.

Robinhood's latest financial instrument intends to let any retail investor feel like they, too, can make money by backing Y Combinator startups.

We tested coolers on camping trips, road trips, beach days, and at parties to bring you our favorite models for every situation. The Yeti Tundra Haul is our top pick.

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TL;DR MCP servers are powerful, but they can provide access to production systems if...

MagSafe accessories make your phone feel uniquely yours. These are our favorites, including Android-friendly Qi2 picks.

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River plans to build a new factory, launch additional models from 2027, and target profitability as it scales production.

Make your product feed visible to AI shopping agents Discussion | Link

This affordable, portable projector doesn’t try to be a cinematic marvel, but it’s fun to use.

A startup has created beagles without the gene that causes runny noses and watery eyes for allergy sufferers.

Mitti Labs plans to expand beyond India and enter the Philippines and Indonesia while growing its carbon credits and agricultural data business.

“Myst” sold over 6 million copies in the ’90s. Its latest sequel couldn’t shore up any funding, pointing to an existential crisis among double-A games.

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The Trump administration's framework for assessing potential cybersecurity risks posed by advanced AI reportedly has no interest in testing open models. Axios reports that not only do the voluntary guidelines outright exclude open models - meaning anyone can download them and inspect their core components - but the framework explicitly says it can't be used […]

People type 2+2 into Google. They type 9+10 . They type 7*8 when they can't remember whether it's 54 or 56. Each of those is a real, high-volume search query — and most of the results are identical calculator widgets. So when I built Wrongulator , a calculator that returns a confidently wrong answer on purpose, I had a question worth asking: what if every expression were its own page, ranking for the exact arithmetic people already search? That is programmatic SEO — generating a page per parameter instead of writing pages by hand. And doing it across 17 languages means programmatic SEO with hreflang, where each generated page also declares its 16 translated siblings. The trap is that most programmatic surfaces are thin, duplicative, and get buried by Google. This one isn't, for a specific reason: every page has a real, unique answer baked into the HTML before any JavaScript runs. This post is about how — and the honest costs nobody mentions. Why a Permalink Per Expression Is Even Possible A page per expression only works if /2+2 reproduces the same result for everyone, forever, with no database behind it. That property isn't free — it's the result of one design decision I cover in detail in why a viral toy must be wrong the same way every time : the wrong answer is a pure function of the expression, seeded by a stable hash, with no per-user state. The relevant consequence here is what that property unlocks for SEO. Because f("2+2") always returns the same wrong answer, the server can compute that answer on demand for any expression in the URL, with zero storage. There's no pages table, no CMS, no pre-generation job. A request for /64+5 runs the engine, gets 67 ("the only correct number"), and renders a complete page around it. The programmatic surface is, in effect, infinite — but it costs nothing to hold, because nothing is stored. The pure function is what makes thousands of unique pages possible without a database. That's the foundation. Everything below is about

Originally published at htpbe.tech . The version on htpbe.tech stays in sync with the latest detection algorithm — refer to it for the canonical text. A large share of fintech still ships on Rails. Stripe, Gusto, GitHub, Shopify, Instacart — the generation of companies that defined modern payments and payroll built their backends on Ruby, and the startups following them keep reaching for the same stack. So when a forged bank statement, an altered payslip, or a doctored invoice lands in an underwriting queue, more often than you would guess it lands on a Rails controller. Your KYC provider already confirmed the applicant is a real person with a valid identity. It said nothing about whether the PDF they uploaded was edited after the bank generated it. That structural-tampering layer is invisible to identity verification, and the right place to catch it is at ingress — before your Document model saves, before the row reaches underwriting, before any downstream system trusts the file. This guide walks through integrating the PDF tamper detection API into a Ruby on Rails application: from the first curl command to an idiomatic HtpbeClient service object built on Faraday, a Data -class result struct, configuration-bound credentials, a typed error class, an ActiveJob that analyzes an uploaded document and routes on the verdict, and a request spec that stubs the API with WebMock. The patterns target Rails 7.x and Ruby 3.x, but they map cleanly onto Sinatra, Hanami, or a plain Ruby worker. Treat the code as a reference architecture: it runs the real request flow against the documented error codes, but you should adapt and harden it for your own traffic profile and threat model. If you want the conceptual overview first, start with How to Detect PDF Tampering Programmatically . Integrating from another stack? See the Python , Node.js , Go , Java / Spring Boot , Laravel / PHP , and C# / .NET guides. TL;DR Two API calls, three verdicts: POST /analyze returns a top-level id , th

Just give it to AI might be the most dangerous phrase in software development right now. I've said it myself. Handed off a task, watched clean-looking code come back in seconds, skimmed it, and moved on because it looked right and the tests were green. Then I reviewed a PR that wasn't mine to write, just mine to check. AI-generated, clean, organized, passing every test I threw at it. I approved it the way I'd approve anything that looked competent on the surface. The bug showed up later. Not in review, not in testing. In production, after the code had already been trusted for a while. Nothing about it had looked wrong. That was the actual problem: it wasn't obviously wrong, it was quietly wrong, in the specific way that only announces itself once real conditions hit it. I went back afterward and sat with that PR properly. Not skimming this time. Actually reading it, actually understanding what it was doing and why, actually treating the review like the real work instead of the formality before merging. It took a lot longer than approving it had. It's the only way I'd have caught it before production did. Since then, I don't rush AI-code reviews anymore. I give them the time writing the code apparently didn't need. And it turns out I'm far from the only one who's landed there. 🧵 The Number That Explains What I Was Feeling According to Harness's 2026 State of Engineering Excellence Report, a survey of 700 engineering practitioners across the US, UK, India, France, and Germany, 81% of developers now spend more time in code review since their teams adopted AI tools . 28% report review time increasing by 30% or more. Here's the trade nobody advertised clearly: AI tools cut time-to-PR by roughly 58%. But those same PRs then sit in review 4.6x longer than before. Review time per developer is up an estimated 11.4 hours a week. The speed didn't disappear. It moved. It went from "time spent writing" to "time spent verifying," and verifying turns out to be the harder, slower h

Is the Future of Enterprise AI Local? For the past couple of years, the standard approach to AI has been pure brute force: take the biggest, most expensive cloud-hosted frontier model you can get hold of and throw it at every single problem. But this "one-size-fits-all" approach is not going to last forever. We are approaching a transition phase where the capabilities of local models will soon pass the threshold of "good enough," just as the growing costs of frontier models become impossible for businesses to ignore. Here is why I believe the pendulum is about to swing firmly toward local, controlled hardware. 1. The Looming API Cost vs. Value Reality Check Right now, companies are actively encouraging their staff to jump on the AI bandwagon. But this honeymoon phase will eventually hit a wall. In the near future, businesses are going to start heavily scrutinising the cost-versus-reward ratio of their AI deployments. Consider the typical enterprise token burn: If an engineer is burning through £10,000 worth of tokens each month on API calls, are they actually adding £10,000 worth of value? Or have they just established an expensive new habit to climb the internal AI usage leaderboard? When CFOs eventually demand budget cuts, teams relying entirely on cloud APIs are going to be forced to unlearn those expensive habits overnight, or find alternative options. Beyond raw cost, cloud providers will likely continue introducing workflow friction. We've already seen instances where providers shrink token allowances, enforce dynamic rate-limiting based on the time of day, or push traffic to lower-tier models during peak utilisation. Building your core workflows on a third-party API means you will never truly control the throttle. This is not even considering the data ownership issue, up until now the only powerful models were closed weight and there was not much choice in ownership, however now that open weight models are available that can offer similar performance and can