AI Platforms

Writing filed under AI Platforms.

Fine-tuning is not a shortcut

Fine-tuning can help, but it works best after the task, data, evals, and product behavior are already clear.

May 19 20269 min read

#fine-tuning #training #datasets

Model routing is a systems problem

Model routing is about choosing the right model for the job while balancing quality, speed, cost, policy, and risk.

April 24 20269 min read

#model routing #systems #latency

Multimodal embeddings made search feel wider

By 2026, embedding work was moving beyond text into image, audio, video, and PDF retrieval.

March 14 20268 min read

#embeddings #multimodal #search

Eval dashboards for real feedback

An eval dashboard turns model examples into a clearer view of quality, regressions, latency, and cost.

March 10 20268 min read

#evals #dashboards #model quality

Multimodal systems need grounded workflows

Multimodal models are useful when they fit into a real workflow with clear inputs, checks, and ownership.

January 15 20268 min read

#multimodal #vision #language models

Synthetic data is useful for evals

Synthetic data can help teams test rare cases, but it should not replace real examples from production.

November 8 20258 min read

#synthetic data #datasets #testing

Guardrails are product decisions

AI guardrails define what a product should do, refuse, explain, log, and escalate.

October 21 20256 min read

#guardrails #safety #product design

Semantic search needs permissions

Semantic search is useful, but production systems need metadata, access control, and clear retrieval behavior.

September 16 20255 min read

#semantic search #embeddings #knowledge systems

Generative systems need constraints

Generative AI is useful when the system has constraints, review paths, and clear expectations for output quality.

April 18 202510 min read

#diffusion #generative models #images

Gemini 2.5 made long context feel operational

Gemini 2.5 Pro pushed long-context reasoning further into ordinary developer planning and product work.

March 31 20256 min read

#gemini #long context #reasoning

AI apps need boring observability

Production AI needs logs, traces, prompt records, retrieval records, and examples that can become evals.

January 23 20256 min read

#observability #ai apps #debugging

Agents need boring boundaries

A practical note on agents, MCP tools, identity, permissions, and why clear limits matter.

October 6 20246 min read

#agents #tools #automation

RAG only works if retrieval is boringly good

Retrieval augmented generation depends less on a clever prompt and more on reliably finding the right context.

September 28 20245 min read

#RAG #retrieval #language models

Vector stores need product discipline

Vector databases help AI systems remember, but production quality depends on chunks, metadata, permissions, and evals.

July 22 20246 min read

#vector databases #memory #retrieval

GPT-4o made multimodal product work real

The GPT-4o launch made audio, vision, and text feel like one product surface instead of three demos.

May 20 20246 min read

#gpt-4o #multimodal #product

Vector search needed product questions in 2023

The vector database rush made semantic search easy to demo and surprisingly easy to ship without product discipline.

September 26 20236 min read

#vector search #retrieval #product