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Our rebranded Boston event, TechCrunch Founder Summit (formerly All Stage), is back on November 4th! And we are looking for some incredible volunteers to help us make this event happen. If you are interested in finding out what goes into building tech events, apply to volunteer. If you are selected, not only will you get […]

Some weeks the complaints about AI are existential. This one they were arithmetic. Scroll Hacker News over the past week — the forum where developers argue about their tools in unusual detail — and the grievances about AI coding assistants weren’t about the models being dangerous. They were about limits running out, bills that don’t add up, models quietly swapped underneath you, and a desktop app eating memory like a browser. And the recurring move wasn’t outrage. It was switching. Quotes sourced from: Hacker News. Every quote below was located at its comment permalink and reproduced verbatim; each is listed with its username, the platform and the date in the Sources section. As always, we quote experiences, not verdicts — a forum comment is one practitioner’s account, often mid-argument, and we’ve framed them as exactly that. What makes this batch worth reading isn’t volume; it’s that the complaints are specific enough to check, and that they keep ending the same way: with a cancelled subscription. “Consumed within an hour”: the limits gripe The loudest theme by far was paid usage limits that vanish faster than the price suggests. On a thread bluntly titled “Quick impressions: A week of using Codex more than Claude,” a user posting as jmaker , on 22 August, described dropping his subscriptions around exactly this problem: “The Claude Pro is consumed within an hour on a simple task.” That’s one account of one plan, but it wasn’t isolated. In the same discussion, roamerz on 21 August traced the arc from happy customer to defector in four sentences: “Then one day I burned through my limit in about 10 minutes and had to get a project completed. I subscribed to Codex and it has been fantastic… I just dropped my Claude max plan down to the pro and subscribed to the $200 plan on Codex.” The specific number matters less than the shape: a heavy user hits a wall mid-task, and the wall — not the model’s quality — is what sends them to a competitor. It’s the lived version of t

During the original US missions to the moon in the 1960s and ’70s, astronauts relied on radio-­frequency systems to communicate, sending grainy, low-quality images and videos to Earth. But on the Artemis II Orion mission in April, spacecraft transmitted crystal-clear footage at speeds on par with those of home internet connections. The livestreams so many…

Many people find AI-based chatbots helpful in keeping up with news, but a study by Pattie Maes and her colleagues at the MIT Media Lab points to a big problem with this strategy. Participants who evaluated paired news headlines and images over the course of four weeks were initially 21% percent more accurate at telling…

When remotely operated vehicles settle on the seafloor or dig through a sand bed, they can kick up clouds of sediment that onboard cameras struggle to see through. Often, the only thing to do is wait until the dust settles. But a new system developed by Amy Phung, SM ’23, PhD ’26, and her advisor…

Oral and forehead thermometers may not accurately capture a person’s core body temperature, and the few ingestible temperature sensors on the market are so big they are hard to swallow and risk obstructing the GI tract. But MIT engineers created one that can send continuous temperature updates at a size of just six by four…

Vaccines that turn the body’s immune system against tumors have shown promise in clinical trials, and a handful have been FDA approved for certain cancers. In many patients, however, these vaccines don’t stimulate enough of a response, and the approach some researchers have taken to strengthening it—delivering the vaccine along with immune-stimulating molecules called cytokines—can…

When Don Yansen ’63 arrived at the MIT AgeLab for a study on technology in care­giving for older adults, he didn’t plan to launch another company. But when he heard participants talk about how hard modern devices can be to use, he decided to develop an alternative. Yansen, a serial entrepreneur who retired to care for…

For Rupert Young ’95, SM ’95, his career in data science and cybersecurity began when his grandfather gifted him thousands of stamps: He built intricate databases to catalogue them, displaying the “precise eye” for detail and nuance that his MIT application essay said would make him a good engineer. Young is now chief product officer…

Even as a teenager, Laurie Stach ’06 says, she had a “crazy ambition to take on the world and solve problems.” At MIT, she realized she wasn’t the only one. “Adults always see these youth who are good at math and science and say, ‘You’re going to do great things—someday,’” says Stach. “Meanwhile, traditional education…

As a first-generation student from a small town, Gohar Khan ’21 had to navigate the college admissions process largely on his own. He founded his YouTube channel, Gohar’s Guide, to make things easier for other young people. Today, more than 10 million people follow Khan on social media for college application advice, study tips, and…

Petroleum-based adhesives are everywhere: bonding the wood and drywall in a construction project, holding together the joints of furniture, and even sticking labels to otherwise recyclable containers. “The labels on a container are held up with petroleum-based glue. And because of that, even though you’re putting the container in the recycle bin, it will not…

Each year, we get a huge influx of applicants to speak at TechCrunch’s events, and this year’s Founder Summit in Boston on November 4 will be no different!

Do fine art and high tech ever converge? At Sotheby’s they do, thanks to Kelly Shen ’17. Shen works in the growing field of art intelligence for the New York auction house. Shen builds algorithms to predict prices, using factors like buying trends and artists’ popularity. She has also worked on such efforts as cataloguing…

Some fortnights the gripes scatter; this one they converged. Across the forums where paying customers of the big AI tools compare notes, the same complaint surfaced against three different companies in the same window: the monthly plan buys less than it did, and nobody dropped the price to match. One firm’s temporary generosity is about to expire, another’s year-old plan has quietly tightened, and the users caught in the middle are doing the same grim sum and reaching for the same coping strategies. Quotes sourced from: Reddit — specifically the subreddits r/ClaudeCode, r/perplexity_ai and r/Anthropic. Every quote below was opened at its permalink and copied verbatim; each is listed with its handle, subreddit and date in the Sources section. We quote experiences, not verdicts — a forum post is one person’s felt reality, and we have framed it as exactly that. The limit that lapses on the 19th The loudest note this fortnight came from Claude Code users watching a date on the calendar. Anthropic had lifted weekly limits by 50% as a promotion, extended it several times, and set it to expire around 19 August — after which allowances fall back to standard. For anyone who had adjusted their workflow to the higher ceiling, the lapse reads as a cut. A user posting as EnthusiasmMountain10 laid out the worry on 14 August: “I’m seeing more tokens burned on tasks that previously felt straightforward, more meandering, and generally less output per unit of usage. At the same time, the 50% usage reduction coming on Aug 19 makes this particularly concerning.” The complaint was double-barrelled: not only is the ceiling dropping, but the same work seems to cost more against it than it used to. That second half — a sense that quality had slipped — ran through the thread. A commenter posting as Captain_Birb put it bluntly: “quality is down.. 4.6 and 4.8 were sharper. Sonnet 5 is seriously a joke — it gets roasted by Opus every time and admits his faults.” Another, TheSassyPlant , descri

OpenAI is quietly testing a feature that lets ChatGPT users pay to undo their own usage limits. Hit the weekly cap on a $20 Plus plan and, for some users, a prompt now appears offering to restore the allowance to full for roughly $8. On the $200 Pro plan, the equivalent reset is reported to run up to about $80. The company never announced it; it was discovered by a subscriber who ran into it at the point of being locked out, and an OpenAI spokesperson later confirmed the company is exploring ways for capped users to buy more usage . The answer-first version: your flat monthly subscription now has a pay-to-continue button, and it shows up at the worst possible moment. The reset restores your usage to 100% and pushes the next weekly renewal about seven days out. It is cheaper than upgrading, which is the point — but it is also a new charge that did not exist a month ago, applied to a limit most users cannot see coming, offered at the instant they are least able to say no. What OpenAI is actually testing The mechanics, as reported, are straightforward. When a ChatGPT Plus subscriber exhausts their weekly message allowance, instead of only being told to wait, some accounts now see an option to pay to reset. Redeeming it restores usage to full and resets the weekly clock. The price sits at around $8 for Plus; on Pro it scales up to roughly $80, still framed as a stopgap against a full plan change. The feature was first surfaced by a Reddit user on the $20 plan who described a black prompt appearing at login once their allowance ran dry — not a setting they went looking for, but one that found them. That detail — a user, not a press release, breaking the news of a paid feature — is itself worth noting: the first public account of how OpenAI plans to charge for extra usage came from someone who had already been charged the inconvenience of being locked out. OpenAI has not disputed the reports. A spokesperson described the effort as exploring ways for people who exhaust the

Some fortnights the complaints scatter across a dozen topics; this one they clustered around two. Across the forums where paying AI users gather to compare notes, two things happened in the same window that left the same taste: the meter moved, and the mark landed. In both cases, something about the product changed without a clear announcement, and the users found out by running into it. Quotes sourced from: the OpenAI Developer Community and Hacker News. Every quote below was opened at its permalink and copied verbatim; each is listed with its handle, platform and date in the Sources section. We quote experiences, not verdicts — a forum post is one person’s felt reality, and we have framed it as exactly that. The $200 plan that lasted two days The loudest note this fortnight was the Codex usage drain. On the OpenAI Developer Community, a Pro subscriber posting as jjjnoronha laid out the damage plainly on 4 August: “I’m on the $200 Pro plan, and my weekly limit was completely exhausted in 2 days of serious development work. This is simply not viable.” He was refactoring a Rust backend with two agents running — one coding, one checking code quality — and described leaving Ultra mode on for three or four hours, which alone consumed 30% of his weekly allowance. But the complaint was not that the work was expensive; it was that the plan was sold with language suggesting far more headroom than what the cap delivered. “When someone pays for the highest consumer tier,” he wrote, “the expectation is capacity for sustained, professional work — not being locked out for the rest of the week after two days.” Others described something stranger. A user posting as kalani reported going to bed with 70% remaining after a Saturday reset, then waking up to find 0%. “Not like I had something running overnight, and yet all usage blocked until this next Saturday,” they wrote on 4 August. “Hard to trust a $200 / month service that is completely unpredictable about whether you’ll even be

I built HermitMQ entirely in Go. The main feature is ditching heavy wrappers like JSON in favor of a custom 29 byte binary protocol. Additionally, data transmission over the network uses a direct file to socket copy mechanism. I will go into detail about the architecture, data storage approaches, benchmark numbers, and show how it is implemented in code. The full source code for the HermitMQ project is available on GitHub: https://github.com/ekhidirov/hermitmq The problem with standard brokers and the cost of serialization When the message counter exceeds hundreds of thousands per second, the main problem for a Go developer is the garbage collector. If every message is parsed via standard JSON, the application starts allocating a massive number of small objects in memory. The GC wakes up too frequently, eating up CPU time and causing network latency spikes. To avoid triggering the garbage collector at every turn, I completely abandoned standard serialization libraries. Every message is packed into a custom header of exactly 29 bytes. In code, the message structure looks extremely simple: type Message struct { Magic byte Timestamp uint64 Offset uint64 KeySize uint32 PayloadSize uint32 RecordCount uint32 Key [] byte Payload [] byte } The first byte is a magic number for version checking and instantly discarding bad packets. Next come 8 bytes for the timestamp in nanoseconds and 8 bytes for the offset, which the broker fills in itself to maintain message order. Then come the key and payload sizes, 4 bytes each. Finally, 4 bytes are reserved for the record count to support batching. The broker reads the stream using the binary package and reuses buffers via sync.Pool. As a result, under standard loads, we achieve practically zero memory allocation. Being honest about allocations and plans for zero serialization To be completely honest: although the broker is incredibly frugal under standard loads, a memory management compromise still remains. An absolute victory over al

Jendrik Jördening shares practical engineering strategies for integrating LLMs into production pipelines. He discusses overcoming non-determinism, restricting schemas, separating semantic text extraction from deterministic code, and validating choices using discriminator models. Learn how to structure LLMs with an MVC approach to ensure database integrity, observability, and system reliability. By Jendrik Jördening