The best blogs about AI that engineers actually read
Best blogs about AI for engineers: a practitioner-curated list with links, a credibility checklist, and a simple routine to turn reading into results. (š RSS bulk-import file included)
You are here because you want a short path to the most useful AI reading without doomscrolling. I was feeling disconnected from the world when I saw how many things were released that I had no idea about.
I wrote this to help engineers and engineering students find high signal sources, set up a repeatable reading workflow, and turn insights into shipped work. I include a clear selection checklist, a time-boxed routine, and a mini case you can replicate.
Iāve grouped the best sources into six buckets. Core labs and platforms. Developers and builders. Agents and automation. Independent analysts and practitioners. Policy, safety, governance. News digests for busy humans.
When a frontier model is released, I try to read the lab post first and only then check secondary coverage. This helps me develop some sense of understanding of the new thing instead of immediately grabbing the opinion of someone else without my own critical thinking. When I am building something, I check blogs like PyTorch, LangChain, or LlamaIndex to get proven patterns.
Just a disclaimer: This is more than you can humanly read every week. I donāt read 100% of the articles in each blog, itād be a full-time job. The idea is for you to create your āreading stackā. Depending on your day-to-day work, some publications will make more sense to you. Iāll leave that up to you!
How I evaluate the best blogs about AI
If you only adopt one thing from this guide, adopt the selection checklist. It saves time and keeps your reading aligned with what you ship.
AI blog credibility checklist
Source authority. Labs, active researchers, senior engineers, or analysts who reference primary papers and experiments. Bonus if they link to code and data.
Update cadence. A predictable weekly or monthly rhythm beats sporadic bursts. Add the cadence to your RSS labels.
Practical depth. Posts that include code samples, configuration details, and failure modes. Screenshots of internal tooling are a strong signal.
Replicability. Clear setup notes, environment info, and evaluation protocol. Look for baseline comparisons and error analysis.
Context and limits. Authors who state assumptions, discuss tradeoffs, and call out when a method will not work.
Fit for role. Beginner, researcher, data engineer, AI engineer, or manager. Do not read everything. Read what maps to your current work.
My time-boxed reading workflow:
Daily ten minutes. Scan lab and digest feeds. Star anything that maps to current work. Skip hype threads.
Weekly sixty minutes. Deep read one developer or agent post. I take notes or prompt quickly in my IDE if itās a new tool to see what the āhello worldā looks like in that tool
Score your sources on a simple three-point scale. I have a Notion database and the extension save to notion to make it easy to capture.
Core labs and platforms
These are primary sources for new research, capabilities, and safety updates. They are ideal when you need facts at the source and links to papers, models, and eval sets.
Who it is for: engineers and PMs tracking frontier model behavior and safety notes
Cadence: frequent
Why I read it: product notes, capability tests, and safety posts land here first
Who it is for: applied ML engineers and students
Cadence: frequent
Why I read it: broad explainers and links to research across modalities
Who it is for: researchers and ambitious students
Cadence: frequent
Why I read it: breakthroughs with clear diagrams and method explainers
Anthropic ā research and alignment
Who it is for: safety-minded engineers and model evaluators
Cadence: steady
Why I read it: interpretability, safety science, and Claude capability notes
Who it is for: engineers working with open weights and large-scale infra
Cadence: steady
Why I read it: open releases, data, and infra insights at consumer scale
Who it is for: systems, agents, HCI, and evaluation curious teams
Cadence: steady
Why I read it: agent standards, evaluation methods, and human factors
Who it is for: performance minded builders
Cadence: frequent
Why I read it: inference optimization, GPU stack details, and practical tuning tips
Who it is for: open model users and contributors
Cadence: frequent
Why I read it: datasets, evals, and community posts that ship fast
Who it is for: data plus LLM platform teams
Cadence: steady
Why I read it: end-to-end pipelines and benchmarks that tie to lakehouse patterns
When a release drops, I read the lab post first, skim linked paper sections for methods and results, then check secondary coverage only to fill in gaps. That sequence avoids hot takes and keeps my notes anchored to primary facts.
For developers and builders
Developer-oriented blogs are where patterns become reproducible steps. This is where I go when code, tracing, and evaluations matter.
Who it is for: model implementers and performance-minded engineers
Cadence: steady
Why I read it: core framework updates, scaling notes, and performance guidance
Who it is for: AI engineers building agent and tool use flows
Cadence: steady
Why I read it: LangGraph patterns, production lessons, and failure mode writeups
Who it is for: retrieval and RAG-heavy apps
Cadence: steady
Why I read it: retrieval plumbing, evaluation examples, and design tradeoffs
Who it is for: teams who want visibility into prompts, latencies, and quality
Cadence: steady
Why I read it: evals, tracing, and MLOps guides you can paste into a repo
Who it is for: training and deployment workflows
Cadence: steady
Why I read it: practical training orchestration with batteries included
Who it is for: enterprise and managed platform teams
Cadence: steady
Why I read it: reference builds and managed patterns that clear security reviews
When I am building or prototyping anything, I live inside these posts. I take small takeaways so I have them in mind in the future. For example, āuse Langgraph to orchestrate a workflow that AI may hallucinate more times than acceptable.ā I add those to my weekly review so it shows up in the next sprint.
Agents and automation
Agentic workflows are moving fast. I track one framework with strong docs, one production story source, and two no-code connectors for quick wins.
Who it is for: engineers testing multi-agent patterns
Cadence: steady
Why I read it: reference framework, examples, and extensions you can fork
Who it is for: builders who want agent teams with roles and tools
Cadence: steady
Why I read it: real-world crews, Bedrock integrations, and clear guides
Who it is for: PMs and engineers who want to automate business processes
Cadence: steady
Why I read it: concrete recipes that connect to the rest of the stack
Who it is for: automation across SaaS tools
Cadence: steady
Why I read it: visual workflows and case studies that map to non code teams
Who it is for: folks who want to see search augmented and multimodal agent research
Cadence: light
Why I read it: experiments at the edge of agent reasoning
Who it is for: teams that rely on answer engines and retrieval
Cadence: frequent
Why I read it: product updates, retrieval notes, and partnerships
Independent analysts and practitioners
Analysts and practitioners challenge ideas published from labs and dev blogs. They also surface system effects and incentives that change how you build. I like having both the original lab post and someone critiquing it to create my own opinion. Thatās the best way to avoid the hype train.
One Useful Thing by Ethan Mollick
Who it is for: managers and individual contributors who want evidence-based guidance
Cadence: frequent
Why I read it: practical experiments at work and in the classroom
Who it is for: security-curious builders and tool makers
Cadence: frequent
Why I read it: LLM security, tooling, open weights, and daily notes
Who it is for: engineers shipping production LLM apps
Cadence: light
Why I read it: system design from first principles and production tradeoffs
Who it is for: students and visual learners
Cadence: light
Why I read it: visual model explainers and reasoning diagrams
Who it is for: leaders who need a model and semiconductor economics
Cadence: frequent
Why I read it: ruthless model economics and capacity takes
Who it is for: AI engineers who want product and devtools interviews
Cadence: frequent
Why I read it: the AI engineer lens and real conversations
Who it is for: readers who want a weekly research and policy scan
Cadence: weekly
Why I read it: concise summary of what matters with sharp takes
Interconnects by Nathan Lambert
Who it is for: engineers who want an inside lab perspective
Cadence: weekly
Why I read it: agents, open models, and organizational context
Who it is for: learners who want researcher-written surveys and interviews
Cadence: steady
Why I read it: long form depth with references
Policy, safety, and governance
Policy and safety shape what you can ship in enterprise and education. Read a balanced set to calibrate claims before you roll features to users. This is for those who want to do more than a prototype.
Who it is for: readers who want evidence-based skepticism
Cadence: steady
Why I read it: careful audits of claims and policy context
Who it is for: leaders and students who want research, policy, and societal impact
Cadence: steady
Why I read it: balanced takes with references and cross discipline input
Who it is for: engineers and researchers interested in evaluation of risk
Cadence: steady
Why I read it: risks, benchmarks, and safety research in the open
News digests for busy humans
Digests are your radar. They keep you informed without pulling you into a feed loop. Use them to pick one deep read per week.
Who it is for: readers who want accessible but serious coverage
Cadence: frequent
Why I read it: careful reporting without hype
Who it is for: readers who want fast product and culture coverage
Cadence: frequent
Why I read it: quick pulse on products and user impact
Who it is for: readers who want a weekly roundup
Cadence: weekly
Why I read it: good signal on research and industry
Who it is for: builders who like a daily stream
Cadence: daily
Why I read it: a broad scan with filters
Who it is for: readers who want bite sized daily summaries
Cadence: daily
Why I read it: compact and consistent
Who it is for: readers who want quick daily hits
Cadence: daily
Why I read it: fast news hits that surface links to primary sources
For quick situational awareness, I skim Benās Bites, TLDR and The Batch each week. I deep dive only if two or more sources align with what I am building.
Quick comparison table
FAQs: staying current without burning out
How do I separate blogs from newsletters without missing updates?
Add both to RSS, but keep newsletters in a separate folder. Use a weekly label so you only open the folder once a week. Create a separate page for best AI newsletters and link it from this guide.
What makes an AI blog credible?
Look for primary links to code and data, evaluation protocols, and posts that show failure modes. Authors who share setup details and constraints are more reliable than marketing style roundups. Also check for the model results in webs like artificialanalysis.ai.
When should I prioritize lab posts over media coverage?
When a new capability ships or a safety issue is discussed. Read the lab post, scan the linked paper, then check a practitioner or analyst for context.
Why follow independent analysts if I already read lab blogs?
Analysts help you see incentives and second-order effects. They often call out costs, data constraints, and product risks that labs do not cover in early posts.
How can I keep up without doomscrolling?
Time box daily scanning to ten minutes. Pick one deep read per week. Log one applied takeaway in your impact log and skip everything that does not map to your current work.
What is a fast way to test if a tip from a blog will help my project?
Replicate one small part with an AI-powered IDE. Thatās the best way to get from zero to one, and if you find an idea that is worth exploring, youāll want to continue instead of stopping at the āhello worldā stage.
How do I start reading all these?
Iāve put here all the links here so itās easier for you
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Some good articles I checked last week. I guess I leaned more into system design!
- . Better to cache on purpose, not by default, only hot, expensive, queries.
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This is a great compilation Fran! Very useful.
Also, thanks for the mention!
I guess you missed https://marktechpost.com/