alzoid

Especially when the solid core now ships with a web ui and API compatibility with OpenAI and Antropic. In my test of ai clients, Ollama was the only one I deleted.

This was my issue with current client ecosystem. I get a .guff file. I should be able to open my AI Client of choice and File -> Open and select a .guff. Same as opening a .txt file. Alternatively, I have cloned a HF model, all AI Clients should automatically check for the HF cache folder.

The current offering have interfaces to HuggingFace or some model repo. They get you the model based on what they think your hardware can handle and save it to %user%/App Data/Local/%app name%/... (on windows). When I evaluated running locally I ended up with 3 different folders containing copies of the same model in different directory structures.

It seems like HuggingFace uses %user%/.cache/.. however, some of the apps still get the HF models and save them to their own directories.

Those features are 'fine' for a casual user who sticks with one program. It seems designed from the start to lock you into their wrapper. In the end they are all using llama cpp, comfy ui, openvino etc to abstract away the backed. Again this is fine but hiding the files from the user seems strange to me. If you're leaning on HF then why now use their own .cache?

In the end I get the latest llama.cpp releases for CUDA and SYCL and run llama-server. My best UX has been with LM Studio and AI Playground. I want to try Local AI and vLLM next. I just want control over the damn files.

Java introduced Optional to remove nulls. It also introduced a bunch of things to make it behave like functional languages. You can use records for immutable data, sealed interfaces for domain states, you can switch on the sealed interface for pattern matching, use the sealed interfaces + consumers or a command pattern to remove exception handling and have errors as values.

I went through evaluating a bunch of frameworks. There was Langchain, AG2, Firebase Gen AI / Vertex / whatever Google eventually lands on, Crew AI, Microsoft's stuff etc.

It was so early in the game none of those frame works are ready. What they do under the hood when I looked wasn't a lot. I just wanted some sort of abstraction over the model apis and the ability to use the native api if the abstraction wasn't good enough. I ended up using Spring AI. Its working well for me at the moment. I dipped into the native APIS when I needed a new feature (web search).

Out of all the others Crew AI was my second choice. All of those frameworks seem parasitic. One your on the platform you are locked in. Some were open source but if you wanted to do anything useful you needed an API key and you could see that features were going to be locked behind some sort of payment.

Honestly I think you could get a lot done with one of the CLI's like Claude Code running in a VM.

I had this issue today. Gemini CLI would not read files from my directory called .stuff/ because it was in .gitignore. It then suggested running a command to read the file ....

I will also add checks to make sure the data that I get is there even though I checked 8 times already and provide loads of logging statements and error handling. Then I will go to every client that calls this API and add the same checks and error handling with the same messaging. Oh also with all those checks I'm just going to swallow the error at the entry point so you don't even know it happened at runtime unless you check the logs. That will be $1.25 please.

I feel like it was this way 10 years ago. Once r/TheDonald successfully gamed the system everyday I think people with interest took notice. Now you can be in a niche sub reddit that averages 40 comments on a post. Then a post that could be adjacent to some hot U.S. political wedge topic gets mentioned and there are 300 comments from users who never take part in the discussion. Even something very general like "students are protesting tuition hikes" the small city I live in gets posted and it gets flooded by people who never comment. If you hit a hot topic like Israel / Palestine, the Ukraine war you see it as well.

Reddit, Fackbook, Twitter, TikTok etc are the places where people get their information and form their options. That why the the wealthy and powerful are buying them outright, or paying to push their influence into every aspect of the conversation. Poisoning the well or "Flooding the zone with shit".

Reddit became what Digg was with MrBabyMan. Or actually something worse.

I asked Claude to add a debug endpoint to my hardware device that just gave memory information. It wrote 2600 lines of C that gave information about every single aspect of the system. On the one hand kind of cool. It looked at the MQTT code and the update code, the platform (esp) and generated all kinds of code. It recommended platform settings that could enable more detailed information that checked out when I looked at the docs. I ran it and it worked. On the other hand, most of the code was just duplicated over and over again ex: 3 different endpoints that gave overlapping information. About half of the code generated fake data rather than actually do anything with the system.

I rolled back and re-prompted and got something that looked good and worked. The LLMs are magic when they work well but they can throw a wrench into your system that will cost you more if you don't catch it.

I also just had a 'senior' developer tell me that a feature in one of our platforms was deprecated. This was after I saw their code which did some wonky hacky like stuff to achieve something simple. I checked the docs and said feature (URL Rewriting) was obviously not deprecated. When I asked how they knew it was deprecated they said Chat GPT told them. So now they are fixing the fix chat gpt provided.

I ran into an AI coded bug recently the generated code had a hard coded path that resolved another bug. My assumption is the coder was too lazy to find the root cause of the bug and asked the LLM to "make it like this". The LLM basically set a flag to true so the business logic seems to work. It shouldn't have got past the test but whatever.

In another code base, all the code was written with this pattern. Its like the new code changed what the old code did. I think that 'coder' kept a big context window and didn't know how to properly ask for something. There was 150 line function that only needed to be 3 lines, a 300 line function that could be done in 10 etc. There were several a sections where the LLM moved the values of a list to another list and then looped through the new list to make sure the values were in the new list. It did this over and over again.

The future is already here. Look at how companies behave today, AI will not change their behaviour. AI will not make them 'nicer'. People talk about the massive productivity change and how we need to think about Universal Basic Income. They don't realize that in the US and other western nations they are already living in abundance (even excess). How do we treat the unemployed and "Unskilled" workforce? Do they have UBI? When they complain about rent and food prices do the wealthy step in to help? Or are they told they should have went to school or acquired a better skill to deserve a better life. What will happen when AI makes while collar workers "unskilled"? The same thing that happens today.

It was my go to back when I was doing Java Desktop / Servelets / Java EE. I found it easier to use than Eclipse, which most people I knew were using. I recently did a Google AppEngine project to collect and display weather data and used Netbeans for dev and Spring for the framework. It still works well, integrates with the package managers and build tools easily enough.

Before Netbeans I was using Textpad with shortcuts mapped to javac. What I liked about Netbeans at that time (2005ish) was that you could press the Run button and your application just ran, weather it was a desktop app or a servelet web app. It reminded me of Visual Studio and the VB6 IDEs.

When the server is behind a pipe of limited size. When most of your new visits happen all at once over a short period of time. When you can't rely on a mobile phones cache like a desktop because 90% of your visitors are using one. A CDN is a easy win to off load resources, even if they are small.

1) Yes, the other benefit at the time was a browser would only do X number of requests per domain, using the CDN allowed more requests. Also it was a bandwidth issue from the server.

2) Yes. I should also mention at the time mobile browser cache was not consistent/reliable.

3) The "loader script" is a basic if(!lib.func) { do the magic }. Its one extra thing I suppose. For the overall page load there are lower hanging fruit.