Ldorigo

How do the economics of your statement work out? Clearly inference providers don't have a time to ROI of 10 years on their hardware costs; and that's without even taking ongoing energy costs into account. What's missing here?

I very begrudgingly started paying for grok for this exact reason. They nailed the voice UI and it works incredibly well with android auto unlike Claude&Gemini (which don't work with android auto at all) and chatgpt (which works well but has hardcored system instructions that make it's voice mode feel like a dopamine deprived Gen Z)

Reddit absolutely has algorithmic feeds since it ipo'ed (maybe earlier but I used third party apps so I wasn't subjected to them). 90% of my home page is bullshit I didn't ask for.

What does "ai in production" even mean? Writing production code ? Depending on how it gets reviewed and the qa mechanisms in place, could be stupid or not. Read only access to production systems and data? Again it depends on safeguards, but probably not stupid, it can be very useful for debugging. Unsupervised write access to production data or infrastructure? Incredibly stupid, but I dont think anyone serious does this.

Why not?

Aside from display quality I wonder if humans are more perceptive to changes in specific colors? Towards the end of the game some examples felt impossible while others were trivial .

Thanks! You mean to be able to search for movies that occurred in a specific set of locations? That's a really cool idea, I hadn't thought about it. The data is there already; it's just not searchable in that way. I might give it a go next week. I need to think of a nice UX for it.

Thank you! I'm fixing some early feedback about the UX and I'll do just that!

Thanks a lot for the usability feedback! I'm trying to improve it based on this and a few other early comments.

Regarding your question, one limitation I'm aware of is that my data consists mainly of movies _made as historical movies_, not movies that just happen to be set in the contemporary period of when the movie came out. Unfortunately there doesn't seem to be an easy way to identify this latter type of movies based only on wikipedia metadata/categories; so the only way I can think of would be to process _all_ movies on wikipedia and infer whether they are in a contemporary setting... but that's prohibitively expensive. I'll think about it more.

Thanks a lot for the feedback, this is what I was hoping to get. After working on something for too long the UX becomes so obvious that it's hard to imagine it from the perspective of someone seeing it for the first time. I'll work on the onboarding. And try to find some history subreddits where I can share this. Thanks for the encouragement!

I do it sometimes (even just through the openai playground on platform.openai.com) because the experience is incredible, but it's expensive. One hour of chatting costs around 20-30$.

What's funny/interesting from a psychological perspective is that several of these made me click (and discover genuinely interesting content) on links that I ignored in the real version. Could you do this everyday please?

I wonder why the focus on replaying UI interactions, rather than just skipping one step ahead to the underlying network/API calls? I've been playing around with similar ideas a lot recently, and I indeed started out in a similar approach as what is described in the article - but then I realized that you can get much more robust (and faster-executing) automation scripts by having the agents figure out the exact network calls to replay, rather than clicking around in a headless browser.

Is it possible to install stuff from GitHub on iOS? I thought it was completely impossible on apple devices.

The data might be the limiting factor of current transformer architectures, but there's no reason to believe it's a general limiting factor of any language model (e.g. humans brains are "trained" on orders of magnitude less data and still generally perform better than any model available today)

Actually, Belgium has two (main) official languages so roughly 50% of the people present are now hearing their native language.

Also, FYI, the majority of "errors" found by your tool are not actually errors.

Do we a know whether the current SOTA foundation models (Gemini, gpt4o, Claude, etc) are actually all GPT-based (as in, causal models)?

As the other commenter said, prompting is everything, and most LLMs are sycophants and will try to do anything you tell them without pausing to tell you "why the hell are you trying to query an API with SQL? That's not what SQL is for". While it's possible to build stuff with llms with little to no technical knowledge, it's still very hit and miss.

With that said, the space is moving incredibly fast and the latest Claude/GPT-o1 are far ahead of anything that was available 3-6 months ago. Unfortunately Claude doesn't allow sharing publicly like ChatGPT, but here is a gist of Claude's answer for +- the same question your friend asked:

https://gist.github.com/ldorigo/1a243218e00d75dd2baaf0634640...

I'm on mobile so it wasn't handy to quickly paste an example API request/documentation for the LLM to follow; so there's a chance it might have hallucinated some if the API parameters - but if I included that; in my experience the code would work on first shot 90% of the time.

Regarding your second query, I'm too unfamiliar with clojure and the two solutions you mentioned to really understand what you were trying to achieve, but if you explain just a little bit more, I'm happy to record a screencast of me figuring it out with llms/genai tools from the ground up. What do you mean with "a container solution that allows for live coding"?

I'm always stumped by comments like this. I'm at a point where ~70% of my code is AI-written, and the majority of the remaining is mostly because it would take too much time to provide enough context to the tool/LLM of choice for it to be able to produce the code I need.

Given the right context and the right choice of model/tools, I think ~90-95% of the code I write could be generated. And this is not for doing trivial CRUD; I work on a production app with 8 other people.

I'm really curious if you could give examples of problems that you tried and failed to use these tools for?

Chatgpt and perplexity both answer that question perfectly fine. Embarrassingly enough, Google's Gemini Advanced (which I got for free with a Pixel Pro phone but would cost 200$+/y) failed to answer it.

I spent a long time looking for one (and also just a transflective/reflective monitor that would work well in full sunlight) and I'm dumbfounded that there's nothing available. We had the tech for this 25 years ago and it's supposedly way cheaper than lcds. Refresh rate and colour quality are lower than LCD/oleds, but surely the vast majority of the working population doesn't care all that much about how true to life colours are? Especially if it means that you can actually _see_ those colours in bright sunlight? I'm hoping something will be available in the coming years as I really enjoy working outside but under the current conditions it's not really possible.

Learnwise.ai | Junior-Mid AI/ML/Backend Engineer (fulltime) | REMOTE (~EU timezone if freelance contract OR Spain/NL for employee contract) | 2-4.5k EUR/month

We build an LLM-based conversational assistant deeply integrated in the Edtech ecosystem. We aim to relieve teachers and admin workers of tedious "easy" questions so they can focus on what's important. Moving towards knowledge management and course support.

Early startup, small, tightly knit team (5 engineers, 2 implementation/process-focused folks, 1 sales guy, 1 marketing gal, 1 chill product-centered CEO). We're slowly scaling and expanding.

Stack (not crucial but you should at least be proficient in python):

- FastAPI python backend - MongoDB - Langsmith (llm evals/observability) - - Next.js (react) frontend - AWS

We're a startup. We need a generalist/"get things done" mentality, and sufficient drive/curiosity to figure things out (and build them/fix them) on your own - we don't micromanage.

About the position: you'd start off supporting me with developing the AI aspects of the product - for now, mainly the conversational interface. This isn't a purely ML position; for now lots of the work is SWE/back-end dev. Projects on the roadmap: building robust testing and observability for LLM performance, experimenting with and improving the RAG pipeline, fine-tuning/distillation where relevant to reduce latency/costs, working on a voice interface.

Experience requirements: ~masters level education in relevant field (doesn't have to be a masters/formal education, but you need to convince us you have an equivalent level of experience/understanding) & 1y of full-time relevant work experience (can be as student job)

WE'RE UNFORTUNATELY UNABLE TO SPONSOR VISAS.

Send e-mail at `luca @ learnwise . ai` with brief intro + CV :)

We're not sure what to do with langgraph yet, interested if anyone here has tried viable alternatives. We could implement a simplified execution graph in-house, but it feels like reinventing the wheel. Also thinking to keep the "graph" part of langgraph but replace all everything else (i.e. tool calls, retrievers, etc.) by non-langchain alternatives.

We're moving our entire codebase away from Langchain and related products (langgraph, langsmith). I originally thought the library had potential and just needed some polishing which would come with time, but it's just been getting worse and worse. LCEL is the weirdest half-baked idea; it's a nice gimmick that makes your code impossible to debug in a step through debugger. Random stuff breaks with every other release. I deeply regret using it by now.

I got so angry with Ryanair's website experience that I'm now routinely paying >100% premium with other airlines to avoid having to deal with it. Off the top of my head:

- They make it sound as if you _have_ to choose a seat (and pay for that choice), to avoid this you must find a tiny text link (not button) somewhere hidden on the page

- All the worst/most expensive options pre-selected for you

- Randomly changing the saliency of the options that give you choice between premium (additional fee) choices and free choices; so that you'll almost certainly accidentally include an add-on you didn't want

- Dynamic pricing that changes depending on whether you've seen the page before etc.

- Ridiculous baggage policies/completely opaque costs for additional luggage depending on where and when you decide to add it to your booking

- At the checkout page, the currency/payment UI is so broken that I regularly had to go into browser devtools and send requests by hand just so I could complete a booking. Also currency exchange scam.

- Clicking on "search" (or was it "book"?) automatically accepts their shitty terms and conditions for you even if you don't click on the checkbox, which is probably illegal

I'm sure there were much more.

Learnwise.ai | Junior-Mid AI/ML/Backend Engineer (fulltime) + Junior-Mid QA engineer (part-time) | REMOTE (EU timezone if freelance contract OR Spain/NL for employee contract) | 2-4k/month

We build an LLM-based conversational assistant deeply integrated in the Edtech ecosystem. Aim to relieve teachers and admin workers of tedious "easy" questions so they can focus on important cases. Moving towards knowledge management and course support.

Early startup, small, tightly knit team (5 engineers, 2 implementation/process-focused folks, 1 sales guy, 1 marketing gal, 1 chill product-centered CEO). We're slowly scaling and expanding.

Stack (not crucial but you should at least be proficient in python):

- FastAPI python backend - MongoDB - Langchain for the AI aspects, slowly moving away from it - Next.js (react) frontend - AWS

We have two open roles. Common requirements: excellent spoken English, a generalist/"get things done" mentality (we're a small startup), and sufficient drive/curiosity to figure things out (and build them/fix them) on your own - we don't micromanage.

- Junior/mid AI/backend engineer: you'd start off supporting me with developing the AI aspects of the product - for now, mainly the conversational interface. This isn't a purely ML position; for now lots of the work is SWE/back-end dev. Projects on the roadmap: migrating away from Langchain, building robust testing and observability for LLM performance, experimenting with and improving the RAG pipeline, fine-tuning/distillation where relevant to reduce latency/costs.

Experience requirements: ~masters level education in relevant field (doesn't have to be a masters/formal education, but you need to convince us you have an equivalent level of experience/understanding) & 1y of full-time relevant work experience (can be as student job)

- Junior/mid QA engineer (part time, can be negotiated). Two main aspects to this: 1. Improve and expand our back-end tests; in particular, add E2E and integration tests 2. Manual QA as part of our release cycle, which you will then slowly automate with browser automation tools (selenium).

Experience requirements: ~bachelor level in relevant field & 1y+ of relevant QA experience OR ~3-4 years of highly relevant work experience.

Send e-mail at `luca @ learnwise . ai` with brief intro + CV :)

I would buy it.

It is indeed completely unfair as Argentina is a single country of 45M people while Europe has ~10x the population and consists of 27countries - the fact that international bank transfers took longer to implement than intranational ones in a single country is kind of irrelevant? Or are you saying that argentina implemented international bank transfers (... With whom?) In only one year?

I got fed up sending cover letters so I made a tool that writes them for me. Scrapes the company website and summarizes it to get relevant background info, takes my resume + arbitrary info I provide as input, and the job posting (can also work without for unsolicited applications). I then fine-tuned a GPT3 model on actual cover letters I had written to make it sound like me, and voilà ! Actually landed me a job.