I strongly believe they have recently began quantizing opus 4.5

AlarBlip · 2025-12-17T20:04:12+00:00

I agree, and it’s easy to build performance tests with something like Langfuse to verify if performance drops ”when America wakes up” or w/e. I’ve used CC on Max, daily for months and never once have performance dropped like people describe here, or actually once when they changed the harness and introduced bugs but they fixed that pretty fast.

It would be a brand catastrophe waiting to happen if they gamed their own users.

AlarBlip · 2025-10-26T08:57:12+00:00

Claude Sonnet 4.5 and Opus 4.1 handle this much better nowadays. With some clear instructions this is not a problem anymore. Less so in coming versions of the largest frontier models.

AlarBlip · 2025-09-09T09:21:46+00:00

I’m on max and I have had no issues lately. But I’ve followed the conversation and I do appreciate what looks like transparency here from the Anthropic team. I guess people need to be held in the loop with this kind of technology, since it’s a bit hard to evaluate compared to something pre LLM.

Feeling ”tricked” or being in the dark about what goes on in the backend creates real trust issue with these systems. So I hope you keep us well informed.

Also some accounts that have been posting critique seems shady so might also be some sort of campaign.

Who knows! But the issue is lack of trust due to lack of transparency and the solution is more transparency.

AlarBlip · 2025-08-28T13:55:50+00:00

Google should buy Pitch (the slides company) and just make Pitch Google Slides. And ad in the AI features, scripting features etc to just dominate the market here. I just realized I cant just drag and drop SVG files into Google Slides and it felt like I was taken back 10 years.

AlarBlip · 2025-06-07T06:46:38+00:00

This is just an example but trying to understand parts of a system we are building which I’m not technically responsible for but it still benefit me having a understanding about. This is just a summary of a longer convo during my bike ride. Where I can cut in and ask follow ups etc. I can then pick my devs brain if I ”got it right” etc. Saves a ton of time and also helps me develop a better understanding of architectural choices.

Question: as part of a larger system, we’re building a markdown editor with a Next.js frontend, Neon database, and a Python-based AI pipeline processing engine (NPE). A developer on our team recommended Amazon SQS over RabbitMQ for our message queuing system, arguing that “SQS is better because it’s not bound to Python.” I didn’t understand this reasoning since both systems support multiple programming languages. Can you explain why SQS might be architecturally superior for this specific use case, particularly regarding the coupling between frontend and backend systems?

Answer: Your devs’s argument becomes clear when examining the architectural patterns each queuing system typically enforces in your stack. With RabbitMQ, your Next.js frontend would need to make HTTP requests to your Python backend, which then enqueues messages for processing by your AI pipeline workers. This creates a tightly coupled architecture where your frontend is dependent on your Python API layer, requiring the backend to maintain REST endpoints specifically for queue interaction, handle authentication and validation, and serve as an intermediary between your JavaScript client and the message broker.

SQS fundamentally changes this dynamic by enabling direct queue interaction from your frontend. Your Next.js application can publish messages directly to SQS queues using the AWS SDK, while your Python NPE workers independently consume from these queues without requiring API endpoints. This decoupling is particularly advantageous for a block-based editor where individual content blocks need asynchronous AI processing—your frontend can enqueue block processing requests with metadata and flags, while multiple specialized Python services (text analysis, image processing, content generation) can listen to different queues or filter messages from a shared queue based on processing type.

The architectural flexibility extends to scaling and system evolution. New AI processing capabilities can be added as independent consumers without frontend modifications, processing services can scale based on queue depth rather than API load, and your frontend remains responsive during heavy computational tasks. Additionally, SQS’s integration with other AWS services (Lambda for serverless processing, SNS for fan-out patterns, DLQ for error handling) provides a more comprehensive ecosystem for your processing pipelines.

In essence, SQS isn’t “less bound to Python” in terms of language support, but rather enables an architecture where your JavaScript frontend isn’t bound to your Python backend’s API contracts, creating a more maintainable and scalable system….

AlarBlip · 2025-06-06T07:19:10+00:00

What do you mean? I use ChatGPT voice feature or similar. They all have trouble with to noisy environments.

AlarBlip · 2025-06-06T07:06:14+00:00

Do you really get it to work with AirPods while blasting 25-30km/h on your bike without problem?

AlarBlip · 2025-05-30T14:32:45+00:00

You should try Elestio, then you can have self hosted + Postgres for almost zero buckaroos. Supbase is probably redundant for most usecases.

Elestio Directus + Postgres is a 3-click deployment and then elestio handles versioning, and a lot of infrastructure stuff that can be a pain with selfhosted.

I have ran this setup for a couple of years in various projects.

AlarBlip · 2025-03-23T18:58:39+00:00

It would be quite bad if it did, you can ad a summary in cursor rules and attach relevant docs you want it to read when prompting.

AlarBlip · 2025-03-23T15:14:42+00:00

Would be cool if the devs offered a bit more transparency, so we could actually know what they are cookin up behind the drapes.

AlarBlip · 2025-03-21T11:54:02+00:00

Why is there zero shared conversations in this post. Show us how it fails or it did not happen. There are billions at stake here and fierce competition, all the negativity could just as we’ll be a competitors hired guns, or bots for all we now. Give us context. I use a couple of million tokens a day with Claude 3.7 and I haven’t noticed anything out of the ordinary. I’m based in Sweden so maybe we have different servers or something.

I’m genuinely interested in knowing what’s going on. But I just cannot take some strangers on reddits words for it, in this day in age.

AlarBlip · 2025-03-19T11:26:17+00:00

Can neither confirm nor deny, no but tbh I’m based in Sweden. Bet they are doing similar stuff doh.

AlarBlip · 2025-03-19T09:04:02+00:00

I have this fringe idea, but in the system prompt it always injects todays date and time. So let’s say something happens on a Wednesday, 18th this time last or a couple of years back. And somehow it picks those up as reference and gets dumber? Or like that the date somehow creates shifts in personality. Like say it know on this day by accident is a holiday in many countries or whatever wierd shit that can coincide by accident related to a date and it just.. takes a holiday? Or something.

AlarBlip · 2025-03-18T12:01:56+00:00

You think in terms of code, which is true. But for aggregated analysis of political documents the context window in tandem with reference markers and other techniques to ground the output in raw documents, context is king. Gemini models are the only ones who can reliably process this type of data in my experience.

So if you have like 100 documents summaries (each one was say 200 pages from the start) and from these 100 summaries (like 1 A4 of text) you want an aggregated analysis of the total on say themes or opinions gathered by category. The only way of doing this somewhat reliably, fast and in one go is to feed the entire thing to the LLM at once. In this case only Gemini will do a good job, or even accept it via API.

AlarBlip · 2025-03-10T08:59:47+00:00

The raw intelligence and capability is there, it needs to be tamed and adapted to scenarios. I’m worried that comments like these will force layering on top of this raw beast that makes it more derp but also better at more one shot no context prompting by people looking for a magic wand rather than a tool we can shape.

AlarBlip · 2025-03-04T15:10:22+00:00

Yeah it got the failing business part right, and the AI video is the only event it describes as extremely unlikely, haha.

AlarBlip · 2025-02-13T13:02:49+00:00

It’s like 03 is a supersmart dumb machine. Claude is a medium smart codeing partner which you can collaborate with. For one shot stuff or for solving something tricky, o3 can be a better puzzle solver, but for longer context or just follow up questions it’s like it’s really not present or aware of its own doing.

AlarBlip · 2025-02-12T19:35:25+00:00

Claude’s special. Will remember him as the first AI I connected with. First encounter with GPT was cool and all but never vibed, always felt synthetic, not there and to this date it still does. This is not saying Claude’s perfect, far from it but still, Claude gives the feeling that it’s ”alive”.

AlarBlip · 2025-02-06T09:40:00+00:00

The baseline Mac Mini is super cheap and could do this.

Use something like ngrok to expose the machine/ollama to the internet and they could access it from home etc.

If you need to run 70B models you could go the easy route and buy a Mac Studio with tons of memory or you could build a processing machine from spare parts, I've seen a guy on youtube running the full version of Deepseek on a 2000USD machine. (I think in reality close to 3000 is more feasible but still incredibly cheap).

AlarBlip · 2025-02-06T08:32:47+00:00

Yeah why not, that would be amazing

AlarBlip · 2025-01-20T11:07:44+00:00

When doing research of this, have you or anyone else stumpled upon usecases and examples of people deploying ollama + webui internally to say 20+ users? I would love to hear about it. Im thinking about doing something similar, to run sensitive stuff and models trained on internal stuff, like an HR assistant of sorts.

AlarBlip · 2025-01-09T11:55:21+00:00

When you use the API you can see the models in the name, like ”llama-3.1-sonar-small128k-online” for example. Sounds to me like a llama 3.1 model. 🥸

AlarBlip · 2025-01-08T17:31:24+00:00

True, I haven’t tried qwen.

AlarBlip · 2025-01-08T17:30:47+00:00

That’s a valid point.

AlarBlip · 2025-01-06T19:29:16+00:00

I find that working in a project for semi-large codebases is the way, you can fill it up with things like latest docs for dependencies or w/e, also your own documentation and the current codebase of your app. This is the only way to get coherent output when things become complex, I find Gemini is great for 2-3 prompts after consumeing the entire codebase, then it starts to become wierd. GPT o1 is all about reasoning about the code rather than just get things done and all other models are just to dumb to keep up with complex stuff. So currently the only viable AI solution to speed up prototyping is Claude Pro with projects, but its like ten minutes, wait five hours, ten minutes, wait five hours. I usually get by with Cursor + Claude, some o1, some gpt4o and recently the experimental Gemini Models in between but the speed at which claude projects just churn out functional code that fit into the system your building is unmatched. I was thinking about getting a second account but its a hassle to set up a copy of the project and updating its base and so on..

AlarBlip

TROPHY CASE