AI Engineer
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Build Agent Evals: Traces to Experiments
Replace vibes-based testing with a full eval pipeline: trace agent runs with Phoenix, categorize failures from data, build code/LLM evals, run experiments to validate prompt changes on a financial agent.
AI EngineerMind the Gap: Observability for Drifting AI Agents
Microsoft Foundry's stack uses OpenTelemetry tracing, built-in evaluators, red teaming, and an 'observe skill' to detect agent drift, evaluate workflows, and auto-optimize prompts—bridging expected vs. actual behavior from build to production.
Build Event-Sourced AI Agents with Stream Processors
Create debuggable, composable agent harnesses using event logs, synchronous reducers for state, and dynamic JS processors appended as events—no servers or deployments required.
Agents Train Models via Hugging Face Skills
Hugging Face skills let coding agents fine-tune VLMs like Qwen2VL on datasets like LLaVA Instruct Mix with one prompt: agents calculate VRAM, pick instances, and launch jobs remotely or locally.
AI EngineerChess Coach Pipeline: Engines + Detectors + LLM Translator
LLMs fail at chess due to hallucinations; fix by using Stockfish for evaluation, tactical/positional detectors for concepts, and LLM only to translate into natural language—achieving sub-3s latency without errors.
CI/CD Breaks for Agents: Use Continuous Compute Loops
Traditional CI/CD chokes on thousands of agent PRs with cache thrash and merge bottlenecks; replace with intent-driven agent loops featuring inline validation, premerge reconciliation, and stateful continuous compute for sub-minute iterations.
Build Stateful Agents with File Systems & AI SDK v6
Give agents persistent sandboxes, bash tools, and memory files via AI SDK v6 to make them follow long tasks, build on prior work, and generate reusable Python scripts without manual context management.
AI EngineerRL Industrializes GenAI Production via Feedback Loops
95% of GenAI pilots fail production because instruction tuning and prompts can't systematically integrate defects and metrics. RL does, enabling smaller/cheaper/faster models that scale to millions in token costs at Fortune 500s like AT&T.
Malleable Evals: Adaptive Testing for Changing AI Agents
Static benchmarks fail self-adapting agents; use production traces for agent-curated, always-on eval suites that self-optimize toward user intent.
Embed Pi Coding Agents via CLI Tools in Products
Pi's minimal TypeScript SDK powers LLM agents that loop tools; expose CRM/ERP data as secure CLIs for natural agent use, as in a B2B sales pipeline routing RFP emails to per-customer sessions that output inbox drafts.
AI EngineerScaling AI Agents to Slack Company Coworkers
Viktor turns personal AI agents into company employees by living in Slack, inheriting one-time integrations for 3,000 tools, isolating memory across channels/DMs, and handling Slack's complex inputs like threads, edits, and drifts—while preserving model personality for user trust.
MLX: Frontier AI Fully On-Device on Apple Silicon
MLX runs real-time vision, <100ms TTS, omni models, 426B LLMs, and text-to-video on 16GB Mac VRAM—no cloud. Turbo Quant cuts KV cache 4x for 1M contexts, enabling accessibility and robots in low-connectivity areas.
Replay Logs Fail Agents: Use VM Snapshots Instead
Replay durability constrains agent code with growing logs; split into context logs (DB durable) and execution snapshots (14MB Firecracker VMs, <1s save/100ms restore) for multi-day sessions.
AI EngineerFix Agent Context with Head/Tail + Memory, Not Summaries
Truncation breaks reasoning by forgetting history; summarization lacks control. Head/tail truncation preserves key context (first/last 100 chars), stores middle in retrievable memory, and offloads heavy tasks to sub-agents for reliable performance.
Close Playground-to-Production Gap with Feedback Loops
One-shot AI features fail in production due to costs, unreliability, and user diversity—build custom tracing UIs and web previews for Electron apps to enable rapid iteration across teams.
TTS Converges on LLM-Style Autoregressive Audio Token Generation
TTS models now use autoregressive transformers to generate compressed audio frames sequentially, solving high bitrate (200kbps) via neural codecs for streaming latency under 17ms in voice agents.
AI EngineerVoice AI's 'Her' Moment Blocked by Latency, Duplex, and Cost
Cascaded voice systems hit 500ms-4s tool delays vs. human 200ms; half-duplex kills backchanneling; full-duplex like Moshi flows naturally but lacks agent intelligence, paralinguistics, and cheap scaling.
Wrap Existing Chat Agents in Voice with ElevenLabs Engine
ElevenLabs' Voice Engine adds voice to any built chat agent via a simple SDK wrapper, handling STT (Scribe), TTS (V3), emotion-aware turn-taking, and interruptions without rebuilding your RAG, tools, or evals.
Agentic Search Powers 80% of LLM Context Engineering
Context engineering relies on agentic search tools to pull relevant data from files, DBs, web, and memory. Master tool descriptions, skills, and shell tools to avoid brittle retrieval—demoed with ElasticSearch and LangChain.
AI EngineerOptimize Live Agents: GEPA Prompts + Managed Vars
Tune production agents without redeploys using Logfire's managed variables for prompts/models and GEPA's genetic algorithm to evolve better prompts from evals on golden datasets.
AI EngineerClone Lib Repos to Make Agents Master Effect Patterns
To get coding agents using Effect reliably, clone its repo as a git subtree into your project. Agents treat it as your codebase, extracting patterns directly from source code instead of vague prompts or docs.
Agent Observability: Signals and Self-Diagnostics
Shift from evals to production monitoring using explicit signals (errors, latency), implicit signals (frustration, refusals via classifiers/regex), experiments, and agent self-diagnostics to catch issues early in complex, non-deterministic agents.
Build AI Skills for Repeatable Agent Tasks
Skills are portable markdown folders with frontmatter, constraints, and scripts that teach LLMs specific, reliable workflows—codifying DRY principles for agents across repos and teams.
AI EngineerMissions: Three-Role Agents Ship Code for Days
Combine orchestrator (plans with validation contracts), serial workers (implement features), and adversarial validators (verify end-to-end) into missions that autonomously execute software projects for up to 16 days without human attention.
MCP Apps: Interactive Branded UI in AI Chats
MCP Apps let tools return interactive HTML UI chunks over MCP instead of text, enabling branded experiences in ChatGPT, Claude, VS Code; interactions route through hosts to stay in context.
SIE: Dynamic Inference for Small Models on Shared GPUs
Open-source SIE engine from Superlinked enables hot-swapping small embedding models (e.g., Stella, ColBERT) on one GPU via LRU eviction, cutting costs and solving context rot in agents by preprocessing data.
AI EngineerRun Gemma 4 Agents On-Device with LiteRT Stack
Gemma 4's 2B/4B edge models enable on-device agents with tool calling, JSON output, and reasoning via LiteRT, delivering low latency, privacy, and cross-platform support on Android/iOS/desktop/IoT.
Build Knowledge Bases from Agent Failures
Assign real enterprise problems to AI agents; their failures reveal exact knowledge gaps. Fill them iteratively to create a demand-driven context base that makes agents semi-autonomous—far better than dumping uncurated RAG data.
Train GPT-2 LLM from Scratch on Laptop
Hands-on workshop: Build tokenizer, causal transformer, training loop in PyTorch to train tiny GPT-2 on Shakespeare locally (16GB RAM) or Colab – reveals core engineering without cloud.
AI EngineerEval-Driven Skills: Boost Agent Performance on Supabase
Use eval-driven development to craft agent skills: define metrics first, structure with progressive disclosure in skill.md, test via Braintrust evals on Supabase workflows, iterate to fix failure modes like unused skills or bad instructions.
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