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| 1 | +- [Staged AI Boundaries - Assignment Case Studies](https://ai-assignment-case.vercel.app/) |
| 2 | + - [[Assignment]], [[Artificial intelligence in education]] |
| 3 | +- [[2602.00070] FoundationalASSIST: An Educational Dataset for Foundational Knowledge Tracing and Pedagogical Grounding of LLMs](https://arxiv.org/abs/2602.00070) |
| 4 | + - [[Data sets]], [[Mathematics education]], [[ASSISTments]], [[Intelligent tutoring system]], [[huggingface]], [[Artificial intelligence in education]], [[LLM training]] |
| 5 | + - [ASSISTments/FoundationalASSIST · Datasets at Hugging Face](https://huggingface.co/datasets/ASSISTments/FoundationalASSIST) |
| 6 | +- [Harness engineering for coding agent users](https://martinfowler.com/articles/harness-engineering.html#HarnessTemplates) |
| 7 | + - [[AI engineering]] |
| 8 | +- [NousResearch/hermes-agent-self-evolution: ⚒ Evolutionary self-improvement for Hermes Agent — optimize skills, prompts, and code using DSPy + GEPA](https://github.com/NousResearch/hermes-agent-self-evolution) |
| 9 | + - [[hermes]], [[autoresearch]], [[self-improvement]], [[AI engineering]], [[AI agents]] |
| 10 | +- [Rigario (@Rigario): "Many are running @NousResearch Hermes Agent now. Here are some practical tips that help a lot, especially if you're coming from OpenClaw: 1. Nightly skill evolution is worth setting up. Link: https://github.com/NousResearch/hermes-agent-self-evolution Pro tip: Add a second cronjob to evaluate the changes so you don't have to. Make sure it stops anything that tries to game the optimization loop. 2. Install Honcho if you're hitting memory issues. It gives proper cross-session recall, memory synthesis, and better long-term storage. Helps avoid repeating the same mistakes or pulling too much context (and wasting tokens). 3. Consider changing the default session timeout and expiry. Especially useful for threads you don't use every day, prevents the agent from losing context unnecessarily. For those migrating from OpenClaw: 4. Expose your OpenClaw agents as OpenAI-compatible endpoints. This lets you run both side-by-side with zero disruption while you transition. Hermes can call them directly, and your existing crons keep working. 5. On day one, start populating your USER.md and MEMORY.md files. Note for OC users: Hermes has a much smaller character limit than OpenClaw, so populate and curate thoughtfully, don't just dump everything in. Quality over quantity helps it learn you faster. 2,200 for memory and 1,375 for user. Hermes works especially well once you integrate it properly into your workflows. Last tip, don't start changing your skin till your agents are actually doing work. You might never stop and go down the rabbit hole... 🤣" | nitter](https://nitter.net/Rigario/status/2039763698584314311#m) |
| 11 | + - [[hermes]] |
| 12 | +- [plastic-labs/honcho: Memory library for building stateful agents](https://github.com/plastic-labs/honcho?tab=readme-ov-file) |
| 13 | + - [[AI memory]], [[hermes]] |
| 14 | +- [JUMPERZ (@jumperz): "karpathy is showing one of the simplest AI architectures that actually works.. dump research into a folder, let the model organise it into a wiki, ask questions, then file the answers back in. the real insight is the loop...every query makes the wiki better. it compounds.. now thats a second brain building itself. i think this is so good for agents if applied right instead of pulling from shared memory every session, they build a living knowledge base that stays. your coordinator is not just coordinating tasks anymore.. it is maintaining institutional knowledge so every execution adds something back to the base. the bigger implication is crazy tho. agents that own their own knowledge layer do not need infinite context windows, they need good file organisation and the ability to read their own indexes. way cheaper, way more scalable, and way more inspectable than stuffing everything into one giant prompt." | nitter](https://nitter.net/jumperz/status/2039826228224430323#m) |
| 15 | + - [[Knowledge base]], [[Knowledge graph]], [[Wiki]], [[AI memory]], [[autoresearch]], [[AI agents]], [[AI engineering]], [[AI Skill]] |
| 16 | +- [The AI Doc’s Falsehoods and False Balance](https://www.aipanic.news/p/the-ai-docs-falsehoods-and-false) |
| 17 | + - [[Artificial intelligence Ethics]], [[Bias in algorithms]], [[Artificial intelligence in society]], [[Misinformation]], [[Panic pedagogy]] |
| 18 | +- [(2) The Ultimate AI Detection Championship](https://davidwsilva.substack.com/p/the-ultimate-ai-detection-championship) |
| 19 | + - [[AI detection]] |
| 20 | +- [Hollywood Just Packaged AI Anxiety and Is Bringing It to Theaters: Enter the Apocaloptimists](https://davidwsilva.substack.com/p/hollywood-just-packaged-ai-anxiety) |
| 21 | + - [[Artificial intelligence in society]], [[Persuasion]], [[Propaganda]], [[Cognitive biases]], [[Panic pedagogy]], [[Misinformation]] |
| 22 | +- [Emotion Concepts and their Function in a Large Language Model](https://transformer-circuits.pub/2026/emotions/index.html) |
| 23 | + - [[Emotion]], [[LLM]], [[Neuron]], [[Sycophancy]], [[Human-computer interaction]], [[Machine Learning]], [[Artificial intelligence]], [[Explainable AI]] |
| 24 | +- [Explainable artificial intelligence - Wikipedia](https://en.wikipedia.org/wiki/Explainable_artificial_intelligence) |
| 25 | + - [[Explainable AI]], [[Reasoning]], [[Neural network]], [[Machine Learning]], [[Neurosymbolic AI]], [[Artificial intelligence]], [[LLM]], [[Explanation]], [[Explorable explanations]] |
| 26 | +- [GPT-5.3 and GPT-5.4 in ChatGPT | OpenAI Help Center](https://help.openai.com/en/articles/11909943-gpt-53-and-gpt-54-in-chatgpt) |
| 27 | + - [[ChatGPT]], [[Reasoning]], [[copilot]], [[codex]] |
| 28 | +- |
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