u/NeverOnEarth

If I have to recommend a book that you should read once in a lifetime, it would be Tuesdays with Morrie, a Book by Mitch Albom.

Maybe it was one of those books that finds you at the right time. Coz I really needed the guidance in my life.

This book follows the story of a journalist and his old, dying professor as they embark on their final thesis journey, reflecting on what truly matters in life. Honestly, reading this book feels like sitting with an elder, absorbing the wisdom they’ve gathered over a lifetime.

10/10 recommendation. What book would you recommend that people should read at least once?

Asking for a friend. Looking for entry-level positions. Basically, for someone who is restarting a career.

I am a technical content writer and a social media strategist. Most of the organizations are now adopting AI for writing and creating strategies, as we all know. As of now, the situation is not so bad, but what if in the future, there comes an AI tool that completely changes the market? And no, I am not ready to hear the answers like AI can't replace human insight, because it is definitely impacting the job market, and we are all hearing the layoffs news. So, how are you planning to cope with it? Should we learn AI or be the traditional digital marketer we used to be and pray to God for some miracle?

Seven years working in and around AI-driven product engineering gives you a decent radar for separating the real shops from the ones that rebranded their website in 2023 and called it an "AI practice." I've directly worked with a few of these, and the rest I've vetted through peer recommendations, technical evaluations, or watching their work up close in the industry. The common thread across all of them is that they have actual engineering depth, not just a polished deck.

Here's my list, happy to hear who else people have had good experiences with:

1. TechAhead

16 years in the business with real production depth across agentic AI, custom LLM development, RAG pipelines, and MLOps. AWS Advanced Partner, SOC 2 Type II and ISO 42001 certified, which matters if compliance is non-negotiable for your use case. One of the few shops where the AI CoE is a real practice, not just a rebranded marketing page.

2. Orases

Strong custom software foundation with solid AI consulting capabilities. They push you to define outcomes before writing a line of code, which is exactly what separates a well-scoped AI project from a six-month prototype that goes nowhere. Good fit when you're still figuring out where AI actually fits in your stack.

3. Edvantis

Solid depth on the data engineering side. The mistake most teams make is hiring ML engineers before their data infrastructure can actually support a model in production. Edvantis understands this problem at the architecture level, not just conceptually.

4. Flyaps

Cover the full AI delivery stack, including generative AI, AI agents, MLOps, chatbots, and take a flexible approach to building from scratch vs. integrating existing tools. That flexibility matters more than people realize when requirements inevitably shift mid-build.

5. NIX United

Dedicated team model that works well if you need AI talent embedded long-term rather than project-by-project. Good option when you need continuity of engineers across multiple phases of a product rather than a one-and-done engagement.

6. BlueLabel

Award-winning generative AI agency with a specific focus on agentic AI solutions. The "GenAI agency" label gets thrown around a lot these days and BlueLabel actually has the case studies to back it up.

7. TELUS Digital

Formerly WillowTree. The acquisition brought enterprise-scale AI transformation capabilities into the fold. Better suited to mid-market and enterprise than early-stage but if you're at that scale and need AI integrated into customer experience infrastructure, they're worth serious consideration.

8. GenAI.Labs USA

Purpose-built for generative AI, ML product development, LLM/GPT integration, and AI strategy consulting. The focus is the differentiator here. They're not a general software shop that also does AI on the side, and that specialization tends to mean faster ramp-up on the fundamentals.

9. Krazimo

Newer but with the right instincts, they treat AI adoption as a strategy and governance problem first, which is the correct framing. Too many teams jump straight to model selection before answering what business problem they're actually solving. Krazimo forces that conversation early.

10. Azumo

Latin American nearshore team, so US time zone alignment without the offshore communication lag that kills sprint velocity. Full AI services spectrum like RAG, LLM fine-tuning, computer vision, NLP, and MLOps. Good option when you need senior AI engineers embedded in your team without a lengthy onboarding process.

I find that Claude deliberately gives long documents and designs to burn up credits. You ask for carousel content, you end up getting the whole design. You ask for a new section in your document and end up getting the whole document edited. And this unnecessarily burns the credits. Eh, this was just a rant. Anyone else facing the same issue?

I've been writing technical content professionally for 7+ years, including docs, blogs, API references, and release notes, the works. So, I figured I'd share the same ideas here that might help others.

These aren't generic "write clearly" tips. These are the things I had to unlearn, relearn, or get burned by before they actually clicked.

1. Ask "what does this sentence do?" before you publish it.
Every sentence in a technical blog should earn its place. If it doesn't explain a concept, move the reader to the next step, or add context that prevents a mistake. Cut it.

2. Precision beats volume every time.
"In order to be able to initiate the process" → "To start the process." Your reader isn't here to admire your prose. They need to do something. Get out of their way.

3. The first draft proves you understood the topic. The edit proves you understood the reader.
I do two passes on everything. First pass: logic gaps, missing context, wrong assumptions. Second pass: sentence-level precision. Treating them as one pass is where most writers leave quality on the table.

4. Know what to cut and what NOT to cut.
This one took me years. A sentence that looks "extra" might be the one that prevents a production incident. Caveats, edge cases, "why this step matters," these aren't fat. A screenshot caption that just repeats what the screenshot shows? That's fat. Learn the difference.

5. If you can't write a clean heading for a section, you don't fully understand it yet.
Structure is a thinking tool. If a section resists being titled, that's a signal. It is most probably a comprehension problem. Go back to the source material.

6. Edit for your least technical reader, write for your most technical one.
The smartest person in the room still appreciates clarity. The less experienced person needs it. You don't dumb things down, but you make them precise enough that nobody misreads them.

I am curious to know which writing habit changed things most for you?

I recently started writing on Substack and, honestly, would like to connect with my like-minded audience, but I have been having a bit of trouble gaining subscribers. What can I do about that? And I have done everything basic articles say, like commenting and engaging with other accounts, being consistent in posting notes, etc.