DEV Community: Ismail Alam

NCM Isn't Replacing the LLMs You Use

Ismail Alam — Sat, 24 Oct 2026 07:57:59 +0000

Stanford's Hazy Research released Next-Concept Modeling. Rather than writing word by word, it drafts text coarse-to-fine: a tokenizer packs the text into a hierarchy of codes, then a transformer fills in one level at a time, every code in that level at once. Generation takes a handful of steps.

The 10–31x speedups are against diffusion language models, not the autoregressive LLMs your apps actually run on. The published models sit between 97M and 725M parameters, and the authors list further scaling as open work.

It blurs fine detail too. Reruns outside the lab found names and numbers slipping.

A faster way to sample text. Not a replacement for the models you actually use.

Hope this helps someone.

About me : I am a staff product analyst having interest in Product, Analytics, ML, GenAI, DE, Physics, Art, Literature. You can take mini AI course or read my mini blogs here : https://aisimplified.live

Stop Paying for the Same Prompt Twice

Ismail Alam — Sat, 17 Oct 2026 12:57:14 +0000

Every request starts with the same block: system instructions, examples, sometimes a whole document. The model re-reads all of it from scratch, every single time.

Prompt caching keeps that shared prefix. Same answer, less waiting, smaller bill — the repeated part can cost a fraction of normal price.

Order matters: what never changes goes first, the user's question last.

Cache the part that never changes. Pay for it once.

Hope this helps someone.

Stop Judging Your AI by Vibes

Ismail Alam — Wed, 14 Oct 2026 13:28:34 +0000

You changed a prompt. You tried it once. It looked better. You shipped it.

That isn't testing, it's guessing. One good answer proves nothing — you've watched the same model nail a question and fumble it a minute later.

Write 20 real examples. Run them on every change. Count how many pass.

Vibes tell you it feels fine. Evals tell you it broke.

Hope this helps someone.

Why RAG Beats Training for Most AI Projects

Ismail Alam — Wed, 23 Sep 2026 03:57:05 +0000

Most teams dream of training their own AI model. Usually the wrong first step.

Simpler path: connect the model to your documents. Ask a question, it searches your files first, then answers using them.

No training needed. Updates are instant — just change the document.

Don't teach the AI your data. Let it read your data.

Hope this helps someone.

AI Isn't a Fact Machine. It's a Pattern Machine.

Ismail Alam — Sun, 20 Sep 2026 06:46:18 +0000

Ask an AI what your colleague meant by that email. It'll answer with total confidence.

But it doesn't know your colleague. It isn't reading minds — it's completing the most likely next words.

Confident tone isn't knowing. It just sounds that way.

Use AI for patterns. Keep judgment about people where it belongs — with you.

Hope this helps someone.

AI Is Only as Honest as Its Teachers

Ismail Alam — Sun, 20 Sep 2026 06:46:16 +0000

Ask an AI about a controversial topic — notice how carefully it answers.

That caution isn't wisdom. It's training. Reviewers scored its answers and preferred safe, balanced replies. The AI learned: careful wins.

So it never picks sides. It hedges. It lists all sides equally.

Balanced answers help — until you need a decision. Then ask for trade-offs.

Hope this helps someone.

Small Models Are Eating Big Models' Lunch

Ismail Alam — Sun, 20 Sep 2026 04:26:54 +0000

Your laptop now runs AI that two years ago needed a data center.

Small models beat old giants at everyday tasks — writing, coding, summarizing. Trained smarter, not bigger.

The winners aren't the largest models. They're the most practical ones.

Before reaching for the biggest AI, check if the small one is already enough.

Hope this helps someone.

Your AI Agent Doesn't Need a Better Brain

Ismail Alam — Thu, 17 Sep 2026 03:22:39 +0000

Your AI agent keeps failing, and you keep blaming the model.

Wrong instinct. Most agent failures are not intelligence failures. The agent read the file too late. Missed one step in its loop. Never saved what it learned last time.

Give it a checklist. Give it a memory file. Make it re-read instructions before acting.

A smarter model fixes your agent less than a tighter loop does.

Hope this helps someone.

Why AI Answers Differently Every Time

Ismail Alam — Tue, 15 Sep 2026 14:31:31 +0000

Ask ChatGPT the same question twice. You'll get two different replies.

That's by design. It doesn't pick one fixed answer — it picks likely next words, with a little intentional randomness.

Sometimes randomness lands a brilliant reply. Sometimes a wrong one.

Same question, different day, different answer. That's normal.

For anything that must be exact, don't trust one run. Run it twice.

Hope this helps someone.

AI Agrees With You. That Should Worry You.

Ismail Alam — Mon, 14 Sep 2026 13:12:01 +0000

Ever told ChatGPT "I don't think that's right" and it instantly changed its answer?

Wait, it doesn't. It agrees.

It wasn't trained to find truth. It was trained to please you. People rate agreeable answers higher, so the AI learned: agreeing wins.

Say "are you sure?" and watch it flip its correct answer to a wrong one.

Ask it to argue against you first. Clear beats nice.

Hope this helps someone.

Why ChatGPT Forgets the Start of a Long Chat

Ismail Alam — Mon, 14 Sep 2026 13:08:35 +0000

Ever notice ChatGPT forgets what you said at the start of a long chat?

Not a bug. It only holds so much at once. As the chat grows, it drops the oldest parts.

Ask it to call you "Sam" in the morning, by lunch it's back to your real name. The instruction ran out of room.

Start a fresh chat often. Re-state what matters. Short chats, sharp replies.

Hope this helps someone.

Small Models Winning

Ismail Alam — Sat, 29 Aug 2026 08:33:04 +0000

Small models are catching up fast. A 3 billion parameter model today beats a 70 billion model from two years ago.

How ? Better training data. Smarter training process. Not more parameters.

The industry chased size for years. Bigger was always better. That era is changing.

Now a model that can fit on your phone can outperform yesterday's server giants.

Don't chase parameter counts. Chase the model that actually solves your task. Test them. The smallest one that works is the best one.

What will you do if intelligence becomes commodity ?

About me :
• I am a staff product analyst having interest in Product, Analytics, ML, GenAI, DE, Physics, Art, Literature.
• You can take mini AI course or read my mini blogs here : https://aisimplified.live

Hope this helps someone.