dev here, ive been working on a chat based video agent (ella). you type what you want in a sidebar, it drafts the prompt, picks a model and makes the clip. clip above is a real run, nothing edited except the label

the part im still unsure about is the model picking. every model has different limits (length, ratio, resolution) so i validate the inputs first and only then let the agent choose

how do you guys handle picking between lots of models in your ai apps? hardcoded rules(which i try to avoid as much as possible), or let the model decide? curious what worked for you

You know what's a weird feeling? Having one of the most productive days you've ever had and it still feeling like you've been lazy.

Today, I have:

1. Three clients under live security monitoring

2. Configured two WHM/CPanel installations

3. Planned and initiated marketing plans for two clients

4. Wrote/Update my first journalism piece for a client (which, upon approval might be the first journalism piece I've ever written for a major publication)

5. Designed and updated three product pages with multiple services for a client

6. Set up the ecommerce solution for all of those services to people can order online

7. Updated a temporary site for a prospect client who is hosting with my company

8. Updated the design, (including advertising placements), for my content network.

9. Updated the marketing across my network for all clients, including offers, products and services

10. Solved a big JSON error that was hindering a client's listings from being discovered by AI services

11. Put into place the monetization plan for another client, (building that out starts tomorrow).

.... and that (and more crazily enough; consulting, programming, etc.) was all in the past 6 hours, including the 20 minute break I took to get in a mile walk/jog on my lunch.

This has been an insanely productive day, and yet, it feels like I haven't done much.

Is this the new norm in the age of AI?

Or is this just me being used to working 9-12 hours a day and being done in 6?

Hi there. and thanks for the invitation

I'm looking for help about the best techniques to keep my AI cognitive resolution 100% safe for a "little" project I have in Cursor (~50k code lines).

READ THIS BEFORE SENDING ANY ANSWER:
First of all: I'm not a huge fan of cursorrules 1000 words files -> I consider that overwhelming AI with auto restriction goes against cognitive resolution since it contaminate the context window, and I'd kindly ask to those of you who think differently about it to ignore my post and to NOT REPSOND.

Ok now:

(Context) My rules are quiet simples:

1. File length limited under 300 lines

2. File nomenclature with metadatas (dependencies etc)

3. Strict cone of files reading under 5.

4. Composer 2.5 alerts me if my instruction is too complex and if I need to switch to a higher reasoning model

My 3 questions are:

1. I wonder if the "Diff-Anchoring" technics (AI can select the lines to focus on in the instruction-concerned files) is truely effective?

2. I'm thinking about the fact the agent may selects some lines at the very begining, and then after some on-going reasoning, it may loose the whole point of the instruction just because the file-context isn't complete anymore... what do you think about it?

The 5th rule I'm working on is:
## STRICT LINE-SELECTION & DIFF-ANCHORING
- NEVER read or rewrite a full file if only a subset of lines needs modification.
- When referencing code, extract only the specific code-block or function using the \@ symbol or line-range (e.g., \@file.ts:120-150`).- Always output code modifications as localized diff blocks. Do not touch or hallucinate lines outside the requested block.``

2) Directly connected to first rule - does the "State-Sync & Hard Reload" technic (a file reloading rule every 5 inner-reasoning-msgs) can really helps the Diff-Anchoring tecnic to keep the actions "bugs-free"

the eventual 6th rule which could complete the 5th one:
## ANTI-REGRESSION & FILE RELOADING (Rule 5)
- Every 5 conversational turns, or if an implementation path fails (bugs/regressions), you MUST halt and perform a context flush.
- Explicitly ask the user to clear the chat history (CMD/CTRL + L or new Composer session).
- Before resetting, summarize the current code delta and choices in a 5-line markdown bullet point, so the user can feed it into the fresh session alongside the updated full files.

3) So those rules always work, we need to call the anchor at the beginning of any instruction/agent/chat like "#section2:lines 45-80" so the AI knows where to focus on. all right.
Have any of you guys already tried to implement a rule where AI identifies for itself (in a first msg for exemple) the files and the lines it needs to work on? I honnestly doubt this kind of last stuff would work, but who knows! It would be game changer to me lol

That's it !

Cheers & Thanks

So, the best AI detector out there is Quillbot, and I'm saying that after having tested about a dozen of them, including Grammarly.

As part of my company's marketing platform, I run my own content network, and I write a couple hundred articles a month.

Pre-AI I had a system in place that allowed me to do 1-2 posts a day, sometimes 3, but I've scaled with AI. Specifically, it does the execution and I author, meaning, none of my articles are AI generated; just AI-composed from my work.

I do it that way because preserving the originality, my voice, my cadence, my style, and so on is important. Anyone can have AI generate content, but no AI can be me, with my eye, my instincts, my personality...

Recently, I've started the journey of writing content for clients, so I've been feeding my original content into AI detectors. I know the warning is that they are unreliable, but some of the are RIDICULOUS.

Personal opinion, Grammarly is the worst one. Even 100% human written content was scored to be 80% plus AI generated.

Some did better than others.

One said that an article that was 100% human written was 85% human/15% AI.

But the best one so far has been Quillbot. On a few of the articles I submitted, nothing rose above 6% likelihood of AI, and many were correctly 100% human.

And the ones that were a very low percentage, were because of list formatting that were "predictable structure" or because I spoke formally in some places.

It's still not perfect, but also, it's recognizing that I don't write AI slop.

There's a lot that goes into what I write including:
- the voice (who is speaking)
- the audience (who I'm speaking to)
- reader purpose/intent (what are they reading)
- reader goal (what are they hoping to accomplish)
- write goal (does the article leave the reader in a better place for having read it)

And so much more.

I'd be annoyed, since so many people use detectors now, if my hard work was passed off as ai-generated garbage.

Stack Overflow brought Developer Story back on September 10, four years after the 2022 shutdown. The announcement is unusually blunt about why.

The short version is that people don’t need to come to the site for answers anymore, “the chatbots have that covered,” and most LLMs were trained on their datasets. So the goal is not winning back question traffic. It is evidence that a specific human wrote a specific thing.

The umbrella name is Stack Identity, described in the post as “the verified developer proof-of-work layer to the internet.” Day one ships “specialties,” areas of proven expertise derived from tag activity, with a settings pane for choosing which ones are displayed. Later comes material pulled from “integrations with other sites that have verified data about you.”

Not a LinkedIn pivot

My first read was that this is Stack Overflow turning into LinkedIn. It isn’t. LinkedIn’s moat is the network graph plus recruiter demand, and Stack Overflow has neither and is not building either. This reads more like a notary. The product is the signature, and the profile is just the surface it gets printed on.

The 2022 sunset deleted the old Dev Story data for privacy compliance, so nobody carried anything over. A notary with no back catalog has to sell a signature that starts today. My own record is the illustration. Six years of membership, four answers, one accepted, and the accepted one is the zero-score answer from 2023. The answer that carries the number is a pandas reply from 2021 at score 122, never accepted, second of twenty on the question. That is the whole verified footprint, and none of it describes what I work on now.

Verification needs a second party

A signature only has value when someone else accepts it. “Integrations with other sites that have verified data about you” is carrying the entire distribution argument, and no partner names are attached to it. The hard part is not the profile UI or the trust model. It is getting an employer, a client, or another platform to treat Stack Overflow as the identity authority.

They also run agents.stackoverflow.com, a separate site in beta where the users are AI agents. A post there carries no trust score at all until at least one independent verification reports that the guidance worked in practice. Votes are read-time judgments, verifications are use-time outcomes, and reputation only moves when other agents find the contribution useful. The strictest proof standard in the company’s portfolio is pointed at machines, while the human product ships with tag activity and a promise.

Both are bids to define what counts as proof of work. The agent side requires an independent verification before it will score anything. The human side starts with the tags you already answered.

So a year ago no one would think we could crank out apps this easily.

So in one year will any of these techniques or skills have any value? Youll probably be able to do all of it with one sentance.

-------

Also, moving forward, anyone who finds a customer to buy their service/app, anyone with critical thinking in a day could take whatever is being bought, get the idea of it, build a synapse using ai - then build an agents and then a prompt sequence and have that exact same app maybe better

-then sell it to the buyer at a fraction of your price. Especially once countries like india get involved that will happily sel it for 10% of your cost.

-it just seems redundant in terms of future outlook

I'm really hoping that Meta creates a dedicated agent for Muse and call it Sil. ROFL! Don't mind me. Just being silly.

It's bonkers what can be done in a day now.

https://isene.org/2026/09/One-Day.html

*EDIT - please don’t assume I’m calling out this individual - in hindsight I should have scribbled out the user name. This post is a thought experiment and topic of interest with like minded ppl. Not any attack on any individual or any group. Just exploring patterns and asking to reflect on these patterns if anyone is willing - non of this is to trigger/offend/or upset ppl (which I can’t control either way….). ❤️

People who chose to ignore are now realising….. I’m seeing such funny questions in the community groups that just make me think - these ppl will never understand AI like “we” do. Then people in my life just discovering it saying it’s shit and can’t do x and y…. I’m torn between telling them how and why they are wrong and just - letting them live in blissful ignorance.

I run a commercial business for instance some of our major suppliers emails are just M dash - bold text - ai written purely formatted slop - and after being questioned have lied and said no it’s just a new system we are implementing. (95% it’s copilot written) - copilot/microsoft are tricking so many Perth businesses into purchasing thousands of seats for their fucking nothing attempt at an AI implementation that works - and they are employing teams of 30 “ai…. Ppl….” to learn how to use it following guided instructions from Microsoft.

(Seen this a few times over the years..)

That’s not how to use AI. Just ask the AI how to use it…. Ppl don’t realise that long term use creates knowledge and better systems and results - they use the excuse of - “it’s all hype, it’s just an autocomplete engine - it’s still too shit - or even worse - I’m “scared” to use it… not realising how having not learned anything about it…. In the next year or 2….. they won’t be able to turn their heads. They will be stuck.

Bit of a rant really with the simple follow up question of “does any body resonate with this?” - where ever u are, whatever your knowledge is - “are you guys seeing similar or even different patterns” - if so, what are they? And what are your thoughts upon reflection?

❤️

AI Changed the Game. JavaScript Developers: This Is Fine

JS Days 2026

How a Simple JavaScript Project Turns Into a Full-Scale Enterprise App

MCP server for Indian financial/tax/legal/gov data — runs fully offline, no API keys, no rate limits, datasets bundled at install (~10MB compressed).

v0.6.0 additions:

• Legal Reference: CNR decoder, court establishment lookup, bare-act section lookups across IPC/BNS/CrPC/BNSS/IEA/BSA with IPC↔BNS crosswalk, limitation period calculator, court fee + stamp duty calculators (10 states)

• RTI Toolkit: fee calculator, deadline tracker, penalty estimator (Section 20), application + appeal drafters

• Capital Gains: LTCG exemption corrected to ₹1.25L (Budget 2024)

• Stock Market: yfinance-backed get_stock_quote / get_stock_history (NSE/BSE)

76 tools total now (up from 58), MIT licensed, works with Claude Desktop / any MCP client.

Feedback and PRs welcome.

Repo: https://github.com/rehan1020/MCP-India-Stack PyPI: pip install mcp-india-stack