So, Google added two AI rounds to their interview process - one to find a bug in a messy vibecoded slop, and another one to use it in a system design task.
They haven't been able to hire a single candidate after this change, because their hard requirement was to use Gemini.
Getting AI to recognize spaghetti carbonara in a photo is a start.
Building an app means answering: Which ingredients? What portions? Are the nutrition values estimated or backed by a food database? Can I look up those foods again tomorrow?
Thatβs what we built at January AI.
Send a photo, scan a barcode, describe food in natural language, or search. Resolve foods to a shared catalog with IDs, servings, calories, and macros.
Store those IDs. Look them up again. Build food diaries, compare nutrition, or find healthier alternatives.
Your app or AI agent can use whichever input fits the moment, then work with the same food JSON downstream.
The catalog includes millions of foods, plus packaged-food barcodes and restaurant menus across the US and Canada.
Try it with your own inputs. Inspect the JSON. Copy a request.
Free to start. No credit card required.
https://t.co/ENEDm77HeX
The actual GOAT of our IT industry who co-created the C programming lang and helped develop the Unix OS. Happy birthday to a true legend whose work shaped modern computing.
Sorry, but asking Jacob about AI extinction risk is like asking your AC guy about climate change.
Not saying it's necessarily uninteresting or wrong per se but letβs keep things in perspective and hear from the full range of expertise across the ecosystem!
Here's a trick for finding bugs and DX gaps in your REST APIs: have an AI one shot a consumer app using the APIs.
Do not tell the AI that the goal is to find bugs or it'll start optimizing for that. Just ask it to build the app.
When done, ask for every issue it hit and explicitly ask for all the workarounds that it built bcs it has silently patched things behind the scenes, added hack code just to get the job done. That's your bug list.
I did this last night with GPT-6 Astra on a few one shot demo apps and it found some hidden issues that tripped it up. I got the list, switched to Opus 5, asked it to verify every single issue independently and then file ticket with details. Result: fewer bugs, better DX.
Claude Opus 5 one shot a Cal AI clone and it COOKED.
Snap a meal -> calories, macros and a blood sugar forecast before you eat
I literally just asked it to build a clone using January AI APIs. I expected to find several issues, not a working app.
Build time: 67 minutes
React Native source: https://t.co/R6mhLmmWOq
Claude Opus 5 one shot a Cal AI clone and it COOKED.
Snap a meal -> calories, macros and a blood sugar forecast before you eat
I literally just asked it to build a clone using January AI APIs. I expected to find several issues, not a working app.
Build time: 67 minutes
React Native source: https://t.co/R6mhLmmWOq
π Introducing DeepSeek-V4.1-Flash: smarter, faster, more efficient.
πΉ Introducing the smallest model in our new architecture family, with native visual understanding.
πΉ Designed for greater capability, faster inference, higher throughput, and scaling to larger models.
1/6
Weβre sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.
The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.
The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
When your customer or design partners give you feedback, focus obsessively on anything they remotely flag. Talk to people raising concerns directly, not just the messenger and don't trust their severity levels e.g. 'this is minor but ...' These flags could be actual requirements in disguise. These should be explicit discussions/negotiations, not settled internally.