61–70 of 550 posts
There is no need to develop this ‘skill’. This can all be automated as a preprocessing step before the main request runs. Then you can have agents with infinite context, etc.
There is no engineering involved in using AI. It's insulting to call begging an LLM "engineering".
LLMs are quite useful and I leverage them all the time. But I can’t stand these AI yappers saying the same shit over and over again in every media format and trying to sell AI usage as some kind of profound wizardry when it’s not.
Assuming that this will be using the totally flawed MCP protocol, I can only see more cases of data exfiltration attacks on these AI systems just like before [0] [1].
Prompt injection + Data exfiltration is the new social engineering in AI Agents.
[0] https://embracethered.com/blog/posts/2025/security-advisory-...
[1] https://www.bleepingcomputer.com/news/security/zero-click-ai...
If I need to do all this work (gather data, organize it, prepare it, etc), there are other AI solutions I might decide to use instead of an LLM.
You might as well use your natural intelligence instead of the artificial stuff at that point.
Earlier quoted context omitted.
Drew Breunig's posts are a must read on this. This is not only important for writing your own agents, it is also critical when using agentic coding right now. These limitations/behaviors will be with us for a while.
They might be good reads on the topic but Drew makes some significant etymological mistakes. For example loadout doesn't come from gaming but military terminology. It's essentially the same as kit or gear.
Definitely mirrors my experience. One heuristic I've often used when providing context to model is "is this enough information for a human to solve this task?". Building some text2SQL products in the past it was very interesting to see how often when the model failed, a real data analyst would reply something like "oh yea, that's an older table we don't use any more, the correct table is...". This means the model was…
If I need to do all this work (gather data, organize it, prepare it, etc), there are other AI solutions I might decide to use instead of an LLM.
You might as well use your natural intelligence instead of the artificial stuff at that point.
In this arrangement, the LLM is a component. What I meant is that it seems to me that other non-LLM AI technologies would be a better fit for this kind of thing. Lighter, easier to change and adapt, potentially even cheaper. Not for all scenarios, but for a lot of them.
Earlier quoted context omitted.
You might as well use your natural intelligence instead of the artificial stuff at that point.
Yes, when all is said and done people will realize that artificial intelligence is too expensive to replace natural intelligence. AI companies want to avoid this realization for as long as possible.
OpenAI’s o3 searches the web behind a curtain: you get a few source links and a fuzzy reasoning trace, but never the full chunk of text it actually pulled in. Without that raw context, it’s impossible to audit what really shaped the answer.
I understand why they do it though: if they presented the actual content that came back from search they would absolutely get in trouble for copyright-infringement.
I suspect that's why so much of the Claude 4 system prompt for their search tool is the message "Always respect copyright by NEVER reproducing large 20+ word chunks of content from search results" repeated half a dozen times: https://simonwillison.net/2025/May/25/claude-4-system-prompt...