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Understanding LLM fundamentals without frameworks

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Understanding LLM fundamentals without frameworks

I was using LLM frameworks everywhere but had no idea what was happening inside them. One day I needed to optimize something and realized I couldn't. Hard truth: I didn't understand the fundamentals, just which framework function to call. So I stripped everything away. No abstractions. Just Python, HTTP requests, and the OpenAI/Anthropic APIs. What I found was anticlimactic in the best way: there's almost nothing there. - "AI agents" are just functions the model tells you to call - "Memory" is literally just a list you append to and send back - "RAG" is search, concatenate to prompt, send it off - "Multi-agent systems" are just API calls in sequence It all clicked after that. Not because the patterns are hard. They're not. In fact, they're trivial. They're just buried under layers of abstraction that make them seem hard. I created 7 modules showing the basics: API calls, conversation state, tool calling, RAG, streaming, prompt chaining. Each one is heavily commented, nothing fancy. Side-by-side examples for Claude and GPT so you can see they're fundamentally the same thing. Now when I use frameworks, I actually know if I need them or if I'm just adding bloat. Repo: https://github.com/jmedia65/learn-ai-right

Visit productView on Hacker NewsOct 24, 2025
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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, apple
94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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