As an AI Engineer. Please learn
>Harness engineering, not just prompt engineering
>Context engineering, not just long prompts
>Prompt caching vs. semantic caching tradeoffs
>KV cache management, eviction, reuse, and memory pressure at scale
>Prefill vs. decode latency and why they optimize differently
>Continuous batching, paged attention, and throughput optimization
>Speculative decoding vs. quantization vs. distillation tradeoffs
>INT8, INT4, FP8, AWQ, GPTQ, and when quantization hurts quality
>Structured output failures, schema validation, repair loops, and fallback chains
>Function calling reliability, tool contracts, argument validation, and idempotency
>Agent guardrails, loop budgets, tool budgets, and termination conditions
>Model routing, graceful fallback logic, and degraded-mode UX
>RAG architecture: chunking, embeddings, hybrid search, reranking, and freshness
>Retrieval evals: recall, precision, grounding, attribution, and citation quality
>Evals: golden sets, regression tests, adversarial tests, LLM-as-judge, and human evals
>LLM observability as a first-class discipline: traces, spans, tokens, latency, errors, and drift
>Cost attribution per feature, workflow, tenant, and user journey not just per model
>Safety engineering: prompt injection defense, data leakage prevention, and permission boundaries
>Multi-tenant isolation, cache safety, and cross-user context contamination prevention
>Fine-tuning vs. in-context learning vs. RAG vs. distillation and when each is the wrong tool
>Latency, quality, cost, and reliability tradeoffs across the full inference stack
>Production failure modes: hallucinated tool calls, malformed JSON, stale retrieval, runaway agents, and silent eval regressions
Our Anthropic bill is about to jump from $400K → $1.4M/yr.
Not because usage exploded, but because we're about to cross 150 seats.
Past 150 seats you're forced into Enterprise tier. Seats stop including any usage, every token bills at standard API rates. At our current run rate that's 3.5x overnight.
Unfiltered thoughts on AI spend:
1. We should spend tokens to grow as aggressively as possible. But most people (me included) aren't conscious of what they're spending.
2. Visibility comes first. People see their personal number and they're shocked. I accidentally spent $4,000 in 3 days in Claude Code.
3. For engineering the spend is clearly worth it. Pay for the best model, it saves more than it costs.
4. For a lot of other roles it's questionable. Apps nobody uses, skills someone already built. No ROI.
5. Spend limits are coming. We already require approval for more tokens on our support team.
The era of token-maxxing is coming to an end.
I'm excited to announce that @Microsoft and freeCodeCamp have teamed up to bring you a new FREE professional certification: the Foundational C# Certification.
📚 35 hours of training
👩🔬 an 80-question exam
📜 a FREE verified cert for your LinkedIn or CV
https://t.co/IRiZbayLHV
Another week, another wave of AI breakthroughs.🤯
The freeCodeCamp community is developing several new courses to teach you both beginner and advanced AI concepts, and all the math you'll need to build your own AI systems.
And we already have dozens of ML courses on YouTube.🏕️
Tertuliando con los panas sobre el "nuevo SAIME" nos encontramos con esta belleza: una función que nos dice que no puedes entrar porque hay N cantidad de personas en cola, pero el número N es un número random entre 15 y 19 mil.
En vez de dedicar su esfuerzo en hacer algo bueno.
¡Hola a todos! Hace mas de una semana lance mi primer articulo en mi blog y queria compartirlo con ustedes. En el les hablare sobre un tema que a menudo se nos pasa por alto a la hora de desarrollar aplicaciones: la internacionalización (i18n). (1/7)
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A Thread 🧵