building stuff with AI, hacking, and def not vibe coding... 🇺🇦 https://xitter.cf/t.co/7qCDYQDmFy and building https://xitter.cf/t.co/QrUHUoO6Sg Taking care of our baby @agent__swarm
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Joined February 2012
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Introducing Open Oncall: an open source workflow that will investigate, triage and propose fixes for your production incidents.
Provider agnostic, because your sleep should not depend on Devin being up.
Powered by @agent__swarm.
The playbook to steal it -> docs.agent-swarm.dev/docs/pl…
AGI is here, aka PR reviews in less than 30s for $0.01 🤑
Just create a workflow using Clairic and Jev to pre-review PRs, and it's nuts.
1. You fetch the changes
2. Use Clairic + Jev + deterministic code to categorize and cluster changes
3. Decide which clusters to deep check -> use luna for those
4. Report with line level granularity
Runs in less than 10s if there's no agent work involved, which automatically gives us an additional green light for PRs that are not critical.
if i ever do an interview again and someone asks me how transformers work ill just get out of there
90% of AI Engineer interviews in 2026 are just these 15 concepts repeated:
1/ Transformers & LLM Fundamentals: how LLMs predict the next token, self-attention, multi-head attention, positional encoding, and why transformers replaced RNNs
2/ Tokenization & Embeddings: BPE, how text becomes tokens, and how vectors capture meaning
3/ Prompt Engineering: system prompts, few-shot learning, chain-of-thought, and structured outputs (JSON/schema)
4/ RAG: chunking strategies, retrieval, context injection, context windows, and why RAG beats fine-tuning for fresh data
5/ Vector DBs & Search: vector databases, semantic search, hybrid search , reranking, and HNSW indexes
6/ Function Calling, Tool Calling & MCP: how LLMs trigger APIs, code and databases, and the standard protocol for connecting them
7/ AI Agents & Agentic Workflows: plan, act and observe loops, orchestrated multi-step pipelines, and AI coding agents
8/ Memory & Multi-Agent Systems: short-term and long-term memory, and specialized agents collaborating on a task
9/ Fine-Tuning: full fine-tuning, LoRA, QLoRA, PEFT, and when to fine-tune vs prompt vs RAG
10/ Alignment & Distillation: RLHF, DPO, and knowledge distillation (big model teaches small model)
11/ Quantization & Model Compression: INT8/INT4, pruning, sparsity, and the quality vs speed vs cost tradeoff
12/ Inference & Serving: prefill vs decode, KV cache, speculative decoding, LLM serving, vLLM, batching, and model routing
13/ Multimodal AI & Vision-Language Models: text, image, audio and video in one system, and models that see and reason about images
14/ Evals & Hallucination Detection: LLM evaluation, LLM-as-judge, prompt evaluation, and catching confident but wrong answers
15/ Safety, Observability & Production: guardrails, AI safety, prompt injection, tracing, cost optimization, and production GenAI systems
Know these 15 deeply and you can crack most AI engineer interviews
