faizshah

This is well intended but I know from experience this is gonna result in you asking “how do you find and kill the process on port 8080” and getting a lecture + “Claude has ended the chat.”

I hope they implemented this in some smarter way than just a system prompt.

> I've had cases where it completely misinterpreted what I was asking for, a very strange experience which I'd never had with the other frontier models (o3, Sonnet, Gemini Pro).

Yes! This exactly, with o3 you could ask your question imprecisely or word it badly/ambiguously and it would figure out what you meant, with GPT5 I have had several cases just in the last few hours where it misunderstands the question and requires refinement.

> It was related to software architecture, so supposedly something it should be good at. But for some reason it interpreted me as asking from an end-user perspective instead of a developer of the service, even though it was plenty clear to any human - and other models - that I meant the latter.

For me I was using o3 in daily life like yesterday we were playing a board game so I wanted to ask GPT5 Thinking to clarify a rule, I used the ambiguous prompt with a picture of a card’s draw 1 card power and asked “Is this from the deck or both?” (From the deck or from the board). It responded by saying the card I took a picture of was from the game wingspan’s deck instead of clarifying the actual power on the card (o3 would never).

I’m not looking forward to how much time this will waste on my weekend coding projects this weekend.

o3 was also an anomaly in terms of speed vs response quality and price vs performance. It used to be one of the fastest ways to do some basic web searches you would have done to get an answer if you used o3 pro you it would take 5x longer for not much better response.

So far I haven’t been impressed with GPT5 thinking but I can’t concretely say why yet. I am thinking of comparing the same prompt side by side between o3 and GPT5 thinking.

Also just from my first few hours with GPT5 Thinking I feel that it’s not as good at short prompts as o3 e.g instead of using a big xml or json prompt I would just type the shortest possible phrase for the task e.g “best gpu for home LLM inference vs cloud api.”

The best one was the 2048 era: https://hn.algolia.com/?q=2048

So what this POS company is doing that’s kind of interesting is they have a mobile app just like Uber Eats/Doordash but it seems to be direct through the restaurant: https://apps.apple.com/us/app/local-by-toast/id1362180579

Actually when I ordered through it I paid $45 instead of $60 via DoorDash/Uber Eats but the order was delivered by Uber.

The reason why I think this version will work is because it’s the same level of convenience in a centralized mobile app so single payment method, single portal to browse restaurants etc.

(I have no association with this company I just was looking into this last week on the train)

Meanwhile I have seen a lot of local restaurants advertising how ordering through their own delivery/pickup service can be 30% less than Uber Eats or DoorDash on their takeout boxes (and consequently in your uber/doordash order). It’s interesting that some of the POS companies are getting into the market: https://www.toasttab.com/local

There’s huge room for disruption in this model when a $12 chipotle order costs the consumer $30 after higher menu prices + delivery fees + tip.

Anything you can do with 1 person these large companies will figure out how to do with 10+ people + a PM + a Director/VP sponsor + 3-6 months of red tape and review and a late game “re-alignment” (rewrite) of the features right before the launch date.

More money is spent at most of these companies coordinating work than actually doing work.

I’ve also thought about this moreso as part of a workflow for quickly prototyping stuff. At least for a lot of the modern JIT languages I believe their startup times will be dominated by your imports unless you go with a fastcgi model. This came up as I started adopting h2o web server for local scripts since it has clean and quick to write config files with mruby and fast-cgi handlers and is also crazy fast: https://h2o.examp1e.net/configure/fastcgi_directives.html

Another place this can be useful is for allowing customers to extend a local software with their own custom code. So instead of having to use say MCP to extend your AI tool they can just implement a certain request structure via CGI.

From what I gather it has an embedded http control plane, yaml/json config for plugins, prometheus integration, and distributed compaction workers on separate, potentially serverless, hosts.

It’s RocksDB but faster because data can be searched while still compressed allowing you to load more records in less cache/ram leading to up to 10x performance of RocksDB. It adds an embedded http control plane as well as supporting other extensions like MyRocks (MySQL) and Todis (redis compatibility).

Or at least thats what I got from it correct me if I am wrong rockeet.

I love this app for bird sounds. I found ChatGPT is pretty accurate for pictures of birds or plants as well.

I had the same experience, my typing speed with two hands is 90-120 but with one hand i can still get 50-70. The hard part is punctuation but with AI these days you could try just prompting and let the AI deal with syntax for you.

I have some thoughts on this (in the context of modern SaaS companies).

The most expensive parts of fixing a bug are discovering/diagnosing/triaging the bug, cleaning up corrupted records, and customer communication. If you discover a bug in development or even better while you are coding the function or during a code review you get to bypass triaging, customer calls, escalations, RCAs, etc. At a SaaS company with enterprise customers each of those steps involves multiple meetings with your Support, Account Manager, Senior Engineer, Product Manager, Engineering Manager, Department Manager, sometimes Legal or a Security Engineer and then finally the actual coder. So of course if you can resolve an issue (at a modern SaaS company) during development it can be 10-100x less expensive just because of how much bureaucracy is involved in running a large scale enterprise SaaS company.

It also brings up the interesting side effect of companies adopting non-deterministic coding (AI Code) in that now bugs that could have been discovered during design/development by a human engineer while writing the code can now leak all the way into prod.

I’ve been doing this as well it also works well when you hook it up to Edgar or feed in investor relations documents or earnings transcripts. You can extract a lot of data at scale for regressions using small models with few shot prompts running locally.

I liked the 2048 HN era: https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...

I think from a business perspective, the hiring pool for Rails is small and younger engineers don’t have an interest in learning Rails (check recent university hackathons). It takes a decently long time (2-3+ months) to upskill a non-ruby engineer to be productive in Rails (although this is dampened by AI tools these days) and many senior non-ruby engineers aren’t interested in Rails jobs whereas you can get an Node or Java engineer to come to your Go shop and vice versa. Rails can also be hard to debug if you work in a multi-language shop, you can’t map your understanding of Java or Typescript over to a Rails codebase and be able to find your way around.

All that being said I still use (and like) Rails, currently comparing Phoenix/Elixir to Rails 8 in a side project. But I use typescript w/ Node and Bun in my day job.

Hmm that’s unfortunate. I’m actually scaling up a data journalism project this month which is why I’ve been looking at these.

I’m curious if you can suggest a happy medium between curl-impersonate on VPS (dirt cheap) vs residential proxy ($8/gb)?

Personally I’m not trying to hit any of those common sites.

It’s not really that expensive, I guess it depends on your use case but it’s only like $4/GB (edit: misleading, see replies): https://oxylabs.io/products/residential-proxy-pool or https://brightdata.com/proxy-types/residential-proxies

Usually you only need some subset of the data per page load if you invest some time looking at dev tools you can probably find the API call you need and save yourself a few MB.

They all offer scraping APIs now too that can be cheaper for certain use cases where you only need a subset of the data that is actually loaded. Like $1.3 per 1k requests: https://oxylabs.io/products/scraper-api/web/pricing or $1 per 1k: https://brightdata.com/pricing/web-scraper

Everyday it is looking better and better to self host. I really don’t feel like ChatGPT $200/mo is 10x better than $20/mo and after trying QwQ 32B myself I just can’t justify paying these AI subscriptions. Gemini Flash is also dirt cheap as an api and Groq is dirt cheap and fast for QwQ and other OSS models.

Yes, the quality of the models is increasing at a slower rate and the race will transition to performance and efficiency.

This is good for self hosters and devs who will be able to run near SOTA models like QwQ locally. I’m near the point where I’m going to cancel my ChatGPT Plus and Claude subscription.

If you’re not already trying to self host, build your own local agents and build your own MCPs/Tools I would encourage you to try it (simple stack: ollama, pydanticAI, fastmcp, QwQ 32B, Llama 3.2 3B). If you don’t have a fancy GPU or M1+ try out QwQ on Groq or Flash 2.0 Lite with the Gemini API, it’s super cheap and fast and they are basically equivalent (if not better) than the ChatGPT you were paying for 16 months ago.

Go to r/LocalLLAMA they have the most info. There’s also lots of good YouTube channels who have done benchmarks on Mac minis for this (another good value one with student discount).

Since you’re a student most of the providers/clouds offer student credits and you can also get loads of credits from hackathons.

I just started self hosting as well on my local machine, been using https://lmstudio.ai/ Locally for now.

I think the 32b models are actually good enough that I might stop paying for ChatGPT plus and Claude.

I get around 20 tok/second on my m3 and I can get 100 tok/second on smaller models or quantized. 80-100 tok/second is the best for interactive usage if you go above that you basically can’t read as fast as it generates.

I also really like the QwQ reaoning model, I haven’t gotten around to try out using locally hosted models for Agents and RAG especially coding agents is what im interested in. I feel like 20 tok/second is fine if it’s just running in the background.

Anyways would love to know others experiences, that was mine this weekend. The way it’s going I really dont see a point in paying, I think on-device is the near future and they should just charge a licensing fee like DB provider for enterprise support and updates.

If you were paying $20/mo for ChatGPT 1 year ago, the 32b models are basically at that level but slightly slower and slightly lower quality but useful enough to consider cancelling your subscriptions at this point.

You should probably change the name, I thought it was associated with Manus usually projects like this will specifically have in the tag line “An Open Source alternative to X.”

Other than that looks very cool, reading through the code now. A lot of these projects very heavily lean on Browser Use.

Same is true for remote work. All the engineers know the return to work policies are dumb but all the decision makers have decided we’re all wrong.

This is awesome, there’s a really nice one in python called prompt toolkit that has some a nice api as well: https://python-prompt-toolkit.readthedocs.io/en/master/

Anyone got a rough estimate of the cost of this setup?

I’m guessing it’s under 10k.

I also didn’t see tokens per second numbers.

There’s a good chance the reason this is public is because he was using gist to transfer ChatGPT code between his personal computer and his government computer.

I think ruby can get popular again with the sort of contrarian things Rails is doing like helping developers exit Cloud.

There isn’t really a much more productive web dev setup than Rails + your favorite LLM tool. Will take time to earn Gen Z back to Rails though and away from Python/TS or Go/Rust.

Personally I use lightsail on AWS and cloudflare cause there is always an off ramp to try some of the fancy stuff but then you can always go back to just using cheap VMs behind cloudflare. You can also put it all behind a VPC and you can use CDK/CloudFormation so that’s also nice.

I gave up on using GCP even though the products like BigQuery are way better just because I got burned too many times like with the Google Domains -> Squarespace transition.

I’m thinking of switching back to a bare metal provider now like Vultr or DO (would love to know what people are using these days I haven’t used bare metal providers since ~2012).

Also, completely unrelated does anyone know what the best scraping proxy is these days for side projects (data journalism, archiving etc.)?

I just wrote up a very similar comment. It’s really nice to see that there are other people who understand the limits of LLM in this hype cycle.

Like all the people surprised by Deepseek when it has been clear for the last 2 years there is no moat in foundation models and all the value is in 1) high quality data that becomes more valuable as the internet fills with AI junk 2) building the UX on top that will make specific tasks faster.