Why your elevator ignores you sometimes, and why fancy touchscreen dispatch is often worse than plain buttons.
What the article says
- The simplest elevators just ride to the top and back down, picking people up along the way.
- Smarter systems score each car in real time and can swap your ride to a different elevator if things change.
- That constant re shuffling is the real trick behind shorter waits, not the algorithm's fancy math.
- Oddly, the touchscreen kiosks that ask your floor before you board often perform worse than old up and down buttons, because once assigned you are locked to one car.
- Wait times depend heavily on time of day, with morning rush being the roughest test for any system.
What HN is saying
- Commenters swap stories of building elevator simulators in school, some as breadboard circuits with motors and buttons.
- A recurring theme is that people press both up and down buttons out of habit or superstition, and others explain there are actually good tactical reasons to do it during crowded rush hours.
- Several people share why destination dispatch struggles, since a passenger gets locked to one car and the system loses the flexibility to reroute when conditions change.
- One thread digs into why elevators do not account for weight or crowding, so a full car still stops on every floor during a rush.
- A few commenters recommend games like Elevator Saga and mention the novel The Intuitionist as fun elevator side reading.
AI providers are quietly locking you in by encrypting your own chat history so you cannot switch models cleanly.
What the article says
- When you chat with an AI, the record of that conversation is increasingly not really yours anymore.
- Providers now hand back encrypted chunks for reasoning, web searches, memory summaries, and multi agent messages that only their own systems can read.
- That means if you try to move to a different model or company, the new one only sees a broken, partial version of what happened.
- The writer argues a real transcript should be fully readable and exportable, so any capable model could pick up where the last one left off.
- They also point out a double standard. Big labs freely train on the open internet but call it an attack when smaller competitors learn from their outputs.
What HN is saying
- Commenters broadly agree this is a slow moving lock in problem, comparing it to a frog in warming water that people are not noticing.
- Several people already work around it by having the AI write plain text notes or plans to a file so the next session, even with a different model, can pick up the thread.
- The sharpest disagreement is over how big a deal this actually is. Some say lost reasoning barely matters since sessions are mostly noise anyway, others call it a serious antitrust level problem.
- One commenter who builds an AI coding tool described switching between two different AI models to keep working when either one hits its usage limit.
- A few readers just wanted to flag that the article's own web page was heavy and sluggish to load.
A tiny, cheap DeepSeek model just got dramatically better at coding, and developers are stunned by the price.
What the article says
- DeepSeek quietly refreshed its cheap Flash model, keeping the same size but retraining it for a big capability jump.
- Its scores on coding and agent tasks now beat the company's own much larger Pro preview model.
- It plugs straight into coding tools like Codex without any extra setup.
- The old model names get retired in three months, so everyone using them is upgraded automatically.
- DeepSeek says a matching upgrade to its bigger Pro model is coming soon too.
What HN is saying
- Commenters are floored by the price, several say they are running huge volumes of coding tasks for just a few dollars a month.
- Many now use this model for most daily coding work, saving pricier models like Opus or Claude only for the hardest problems.
- The sharpest pushback is privacy, some refuse to send code to Chinese servers given local data laws, even though the price and speed are tempting.
- A few are annoyed DeepSeek swapped in a much better model under the same name instead of bumping the version number.
- Others question whether the benchmark scores reflect real world coding quality or are just optimized for the tests.
DeepSeek just released a model that matches the big labs for a fraction of the price, and it runs on a beefy home rig.
What the article says
- DeepSeek released weights for V4 Flash 0731, a smaller model that scores well above average on independent intelligence benchmarks.
- It costs a fraction of what rivals charge, well under a dollar per million tokens either way.
- The catch is it talks a lot to get there, using roughly double the tokens of similarly capable models to finish a task.
- It comes with a huge context window, so it can hold a lot of text in mind at once.
- The gains reportedly came from more training and tuning rather than any new architecture.
What HN is saying
- Commenters are genuinely impressed that a model this cheap performs this well, with several calling it the best price to performance ratio they have seen.
- The biggest gripe is verbosity. People note it burns far more tokens than rivals like Gemini Flash to do the same work, though others argue token count matters less than total cost per task.
- A good chunk of the thread is people planning to run it locally, since a beefy but attainable home setup can fit the quantized weights.
- Several see this as part of a broader pattern of Chinese labs undercutting Western pricing, with mixed feelings about whether that is good for competition or a warning sign.
- One sharp correction, people pointed out the weights are on Hugging Face but the license still isn't truly open, it is closer to source available.
YC built a work agent that gives every employee their own scope, then lets them team up with it
What the article says
- QM is an assistant built for companies rather than individuals. Each person and each shared channel gets its own memory, files, and permissions, so people can work alone or pull the agent into a group chat.
- It runs in Slack and on the web with the same identity in both, and it is not tied to one model. Claude Code, Codex, and other tools can all sit underneath it.
- It can search your notes, email, and databases together, triage your inbox with drafted replies, or work inside a real codebase running tests and opening pull requests.
- Admins pick how cautious it is, from pausing for approval on every action to running unsupervised, and everything it does is tied back to the person it acted for.
- The project only accepts contributions as plain written descriptions of an idea, not code. The team says they would rather read your plan than review AI generated code.
What HN is saying
- The biggest reaction was to a leaked internal prompt banning AI design cliches like em dashes and beige minimalist palettes, which turned into a debate over whether that just invents a new kind of sameness.
- Several people asked why not just use Claude's own Cowork. Answers ranged from wanting to run your own models to distrust of Anthropic.
- Others are already running similar always-on agents for real chores, like fixing broken builds, writing incident postmortems, or flagging slow database queries.
- A few dismissed it as a rushed internal tool dressed up for release, with one comment calling it astroturfing.