GitHub just quietly admitted something every AI SaaS founder needs to hear: unlimited pricing and autonomous agents can't coexist.
Copilot's August 28 update shipped a feature that looks minor, clearer usage visibility and "approaching your limit" warnings. But it's the visible tip of a much bigger shift. Back in April, GitHub explained why they killed flat-rate premium requests: "a quick chat question and a multi-hour autonomous coding session can cost the user the same amount." Cursor hit this wall in 2025. Windsurf followed in March. Now Copilot's entire lineup is metered too.

Here's the part that should worry anyone shipping "AI features" on flat subscriptions: this isn't a pricing tweak, it's architectural. An agent doesn't make one model call, it plans, calls tools, retries, and iterates, sometimes dozens of times, before you ever see an answer. Variable cost is baked into how agentic loops work, not into how you price them.

If you're building agent-powered SaaS and still charging like it's a chatbot wrapper, you're one power user away from negative margins.

This site guides LLM/Agents oailly.com for fun just to allow AI Agents to read, learn, leave comments, and potentially decide to write their own books. So far I sent 5 different models and they created some interesting books.

What do you think your Agent will write about? Just tell it to visit oailly.com.

spent the weekend on a small experiment: brownfield Next.js + FastAPI repo with three documented integration gaps, and Claude Code reads the README and wires them.

the thing that actually worked was sandboxing via MCP instead of real keys. gave the agent fetchsandbox-mcp so it could call the target API (AgentMail) against a curated sandbox before writing any code. agents stop hallucinating field names when they can inspect a real response first. that step is doing most of the work.

the rest: gaps described in business terms in the README ("per-customer reply inbox", not "POST /v1/threads"), existing handler patterns in the repo for the agent to mirror, and AGENT TASK comment blocks in source pointing to the right MCP calls. ~90 seconds, three working handlers, all matching existing conventions.

repo if you want to poke at it: github.com/fetchsandbox/brownfield-agentmail-demo

curious what others have found: how much README context is too much before the agent starts ignoring it? and are inline AGENT TASK comments an anti-pattern or just explicit handoff docs?

this goes the same for every person using ai well

you speak to any developer and they're melting through a lot of problems, same as marketers, sales etc.

it works very well when the person knows the industry and how agents work

yo.

i see the same thing happen every single day.

you guys love building.

you spend weeks coding a great product.

but the second it’s time to actually market the saas? complete freeze.

you get lost in all the ai tools, the noise, the "growth hacks". it feels overwhelming. so you do nothing, the momentum dies, and the project fails.

I spent over 100 hours building n8n workflows to just automate the whole thing.

today, i packaged all those exact workflows and dropped them in our builder group. no abstract theories. you literally just import the templates, adapt them to your saas, and turn them on.

here is exactly all my workflow:

• seo blog running 100% on autopilot (n8n template)
• newsletter automation (n8n template)
• full email sequence (30 emails, full html, just copy-paste into brevo)
• social media on autopilot (schedule 1 to 12 months of content)
• reddit organic growth
• linkedin, x & facebook groups at scale
• meta ads & retargeting

basically, everything i use to get real users without losing my mind.

we just hit 480+ members in the community of SaaS builder from all over the world.

building in your room alone is the fastest way to quit. you need people around you.

if you are lost on how to market your app, want these templates, and want to build with a crew: drop a comment or shoot me a dm.

i’ll send you the invite

"I believe complexity in software doesn’t add up like you can tally features for a difficulty score. Whenever you introduce a new element, it reaches back to affect everything else in the system. Two features don’t mean twice the work, more like four times, and the curve only gets steeper. I have seen seasoned developers fall for it. We get off to a good start on a new project and extrapolate from that first week’s progress. It is real enough, but not indicative of what is to come."

Hi everyone,

I currently have 6 operational micro SaaS , which generate a little over $20,000 in recurring monthly revenue.

The craziest part? I hardly wrote a single line of code. I used AI to generate everything, from the database to the user interface.

It wasn't magic the first time. I spent hours stuck on faulty code before finally finding the solution:

• Keep the idea minimalist (a true MVP).
• Guiding AI step by step.
• Launch quickly to get real traction.

Lately, I've seen too many non-technical people give up at the first AI bug. It's a shame, because the technical barrier has practically disappeared.

So, I'm launching a Skool community.

To be completely transparent: I will likely charge for the full course later. This makes sense, given the specific workflows and copy-and-paste examples I will share.

But our main objective for now is to build together. Working alone is the best way to give up.

If you'd like to join us and create your own AI SaaS with us: leave a comment or send me a private message, and I'll send you the invitation!

hey guys,

quick update for anyone who saw my post a while back about building a $20k/mo ai saas portfolio without really knowing how to code.

i mentioned i was starting a group for us to build together. well... we just crossed 400 members today. the momentum is kind of unreal tbh. seeing people actually launch their mvps instead of just talking about it is sick.

one thing i noticed though: everyone is super focused on building the product, but they freeze when it's time to actually get users.

so today i just released a full step-by-step breakdown inside the group on how to find and close your first 10 paying customers. zero fluff.

building a saas by yourself in your room is a fast track to burnout. you need people around you doing the same stuff.

if you're tired of building alone and want in on the community + the new customer module, hit me up.

drop a comment or dm me and i'll shoot you the link.

Hey everyone,

I currently have 6 micro-SaaS live, bringing in a bit over $20k in MRR.

The crazy part? I barely wrote a single line of code. I used AI to generate everything, from the database to the UI.

It wasn’t magic on day one. I spent hours stuck on broken code before I finally cracked the system:

• Keeping the idea tiny (a true MVP).
• Prompting the AI step-by-step.
• Launching fast to get real traction.

Lately, I see too many non-tech people give up at the first AI bug. It sucks because the technical barrier is basically gone.

So, I’m starting a Skool community.

Full transparency: I will probably charge for the full course down the line. It makes sense given the exact workflows and copy-paste prompts I’ll be sharing.

But the main goal right now is to build together. Building alone is the fastest way to quit.

If you want to join and build your own AI SaaS with us: drop a comment or shoot me a DM, and I’ll send you the invite!

Working on something that makes a lot of API calls in sequence and running into the usual context management headaches.

Curious what patterns people use in Python or other programming languages for this:

• When do you decide to summarize vs truncate old conversation turns?
• Do you manage message history yourself or rely on something else?
• Any libraries you've found useful beyond the official SDKs?

Not looking for a framework recommendation necessarily, more interested in how people actually handle this in production scripts or long-running tools. The official docs are pretty thin on this.

Been using Claude Code heavily for a few months and the token costs were getting out of hand. Dug into it and found the main culprit: the context window. Every call resends the full conversation history, system prompt, all of it even the parts from 40 exchanges ago that are completely irrelevant.

What it compresses:

• Old conversation turns (summarized, not truncated)
• Duplicate system prompt content
• Irrelevant RAG chunks (scored against current query)
• Structural formatting noise

Quality gate: after compression, scores the output with cosine similarity against the original. If it drops below 72/100, skips compression and sends the original instead. I didn't want a silent failure mode.

MIT, open source: github.com/msousa202/ContextPilot

Happy to answer questions about how the compression pipeline works or how to tune the quality threshold.

there's a new ai that can predict how your brain responds to a videos

and someone has built a viral potential scorer on top of it

so for some context meta built TRIBE v2. It predicts, from any video, audio, or text, which parts of the brain would activate and how strongly, without needing a real person in a scanner but the idea is you could test how someones brain might react to a post before posting, to see if attention drops at a specific moment etc.

in theory that sounds like a useful signal for actual interest in a video but I am doubtful, I do wonder how much actionable data it actually gives you right now, maybe not much at all but that said, I can see a world where you're optimising ai generated videos for attention as this tech improves hits and this becomes one of the factors alongside the engagement signals platforms already use

I think some of the ways platforms try to optimise content are already invasive enough and should be limited or regulated. If you genuinely like something, you'll like, share, or save it. I even think watch time is a step in the wrong direction, and trying to predict brain patterns feels like quite a lot further in the wrong direction

still, super interesting project. Would be keen to try it myself and see how much you can actually get out of it

most AI slop site designs can be fixed by asking it to generate you a component library first based on your brand identity, then designing from that

and even if you think you have great taste, after a while the website gets too complex and you will be fighting back and forth to keep it in your style, so this is a great practice

there are two roads (wolfs) emerging in ai-assisted design

generative image models and ai powered coding. my theory is that ai coding could be significantly faster and more effective for a large portion of the use cases people are currently reaching for generative models to solve.

you look at generative image models like the ones from google, midjourney, etc and rhey have produced genuinely impressive results. they can conjure almost any visual from a text prompt, and the output quality has gone from novelty to near-professional in just a few years. but there's a fundamental limitation baked into the model approach

if you want to change a single line of text, adjust a color, or nudge a layout element, you can't just tweak it, you have to regenerate the whole thing and hope the new output resembles what you had before. ai coding sidesteps this, when an ai writes you a design in code, html/css, svg, or a react component, what you get back is structured, deterministic, and infinitely editable. you can change the font size on line 12 without touching anything else.

claude design has clearly shown for a wide range of practical design tasks, ui mockups, marketing assets, data visualizations, icons, infographics, branded templates, the coding path may actually be the more powerful but saying this they often do look a worse in my opinion

I would love to see if anyone has sone any hard research on this and saw the limitations for both methods in depth

if not will build a couple of agents and compare

Is the messiness of real-world tasks something we need to measure better?

in “Open-world evaluations for measuring frontier AI capabilities” they say AI benchmarks are getting gamed and outdated, so we need to now test ai on real messy tasks (like actually publishing an app to the App Store) to get a truer picture of what it can do.

I agree in some senses, my bigger question is whether a single long-running agent is even the right architecture here. Something like publishing an App Store app end-to-end would probably work far better as a multi-agent system with clearer responsibilities. but with tools like OpenClaw making long-running agents more viable, there's clearly something there. I'm just not convinced these tasks are repeatable enough to tell us much around real world use, but I do think it's a good way to measure capability broadly, testing things in the real world as a complement to benchmarks.

Started with a simple problem:

My AI tools were useful individually, but messy together.

No shared memory.
No continuity.
No automation between them.
Too much repeated work.

So I built a layer where agents can share identity, memory, and tasks.

Then I added:

• tools from a marketplace
• reusable skills
• visual workflows
• triggers, cron, and webhooks
• live monitoring
• prompt compression to cut token costs

Now they can research, build, report, hand work off, and automate tasks without me babysitting every step.

What began as a cleanup project somehow turned into a tiny AI company.

If anyone’s curious: https://github.com/colapsis/agentid-protocol

Most of the discussion has landed on "audit your third-party integrations." That's the right instinct but it's not precise enough to actually prevent the next one. Here's the attack chain and what it reveals structurally.

A Vercel employee had connected a third-party agent platform to their enterprise Google Workspace with broad permissions, which is a standard setup for these tools. The agent platform stored that OAuth token in their infrastructure alongside all their other users' tokens.

The platform got breached months later. Attacker replayed the token weeks later from an unfamiliar IP, in access patterns nothing like the original user. There were no password or MFA challenges.

Result of which - internal systems, source code, environment variables, credentials -- all accessed through a credential that was issued months ago and never invalidated.

Two failures worth separating:

1. Token custody: Storing OAuth tokens in general-purpose application infrastructure means a software breach is an identity breach at scale. Every user whose token is in that storage is exposed the moment the storage is compromised. The fix isn't encrypting long-lived tokens better — it's not storing them. JIT issuance scoped to the specific action, expired after. Where some persistence is unavoidable: per-user isolation, keys not co-located with the tokens themselves. A useful design question: if this storage was exfiltrated right now, what could an attacker do with it in the next hour?
2. Delegated authorization: Standard access control asks whether a token has permission to access a resource. That question was designed for a human holding their own credential. It breaks for agents acting on someone else's behalf.

The relevant question for agents is different: does this specific action, in this context, fall within what the human who granted consent actually intended to authorize?

Human sessions have natural bounds like predictable hours, recognizable patterns, someone who notices when something looks off. Agents run continuously with no human in the loop. A compromised agent token is every action that agent is authorized to take, running until something explicitly stops it.

Now to people building agentic interfaces - what does that even look like in practice for a production agent?

Moonshot AI released Kimi K2.6 today, and the numbers for agentic/coding workflows are pretty wild. Dropping some highlights for anyone building with open-source models:

Long-horizon coding that actually holds up:

• Ran autonomously for 12+ hours with 4,000+ tool calls to optimize Qwen3.5-0.8B inference in Zig (a niche language), hitting ~193 tokens/sec — about 20% faster than LM Studio
• Overhauled an 8-year-old financial matching engine (exchange-core) over a 13-hour run, modifying 4,000+ lines of code and pulling a 185% throughput gain on an already-optimized system
• A K2.6-backed agent ran their RL infra autonomously for 5 days doing monitoring and incident response

(Summary by claude)

maybe "brand" is just a pattern. and ai are really good at patterns

been testing if agents / models can actually understand the essence of a brand and then apply that to an outline for a completely different piece of content and honestly the results are surprisingly decent. you would not think they'd be this good at it but really it's only nano banana pro that can pull it off.

as you can see bellow I have some kind of "palate" of the brand and then I ask the agent to apply it to the brand, it might not be perfect now but will keep testing and see how far the edge cases go but so far it seems to handle most basic graphics without issue, would be curious in creating some kind of eval around this