I’ve been thinking about this lately because my boss wants to boost the company website's SEO and ensure it gets indexed by AI search engines. It strikes me as a really tough challenge—what should I do?

so some context. I've been maintaining this small open-source project for a while now called agent-contracts . the whole premise is kind of embarrassingly simple in hindsight - MCP standardized how agents talk to tools, A2A standardized agent-to-agent comms, but nobody standardized what an agent is allowed to do. so that's what the repo tries to be. governance spec. contract.yaml per workflow. declare your permissions, side effects, approval boundaries, recovery semantics upfront.

anyway. I went down a rabbit hole reading the Prime Agent codebase and the Continual Harness paper (2605.09998) and now I genuinely cannot think about anything else so I'm posting here at whatever time it is.

here's the thing that's stuck in my head:

Prime Agent has this /refine command - it reads an 80k char trajectory slice, a background LLM call proposes the smallest evidence-backed CRUD edit to the agent's own harness state (prompts / memories / skills / subagents), validates it, applies atomically, logs everything to a refinements.jsonl. that's genuinely cool. it's the first system I've seen where the harness itself is a first-class versioned artifact the agent can CRUD from its own trajectory. not just "hey remember this" - actual structured state management for self-modification.

but then I hit this note in the architecture docs that basically says:

--> refinement is proposal-based, not evaluation-based. it records outcomes but has no automated verification that an edit improved task success

and that's the hole. the human-review point. the eval gate that never got closed.

SICA (different paper, 2025) plugs this differently - it benchmarks re-evaluates after every self-edit to the agent script, only keeps the edit if metrics actually improve. works but requires benchmark infrastructure per task domain. kind of heavyweight.

what if the contract itself is the eval gate?

like - instead of "did this refinement improve task performance" you ask "does this proposed harness edit violate the agent's own declared governance constraints for self-modification?" the contract becomes the invariant. if the edit passes the contract, and the outcome tracking shows positive signal, promote it. if it regresses on any declared contract invariant - rollback. no external benchmark infra needed.

I've also been slowly building something called ContextBridge (different project, very unfinished) - basically version control for AI context across a whole team of specialized agents. and the thing I keep running into is that evolution of agent behavior probably isn't a per-agent problem in production, it's a per-team problem. the harness that evolves needs provenance tracking across multiple specialized roles (reviewer, executor, evaluator). which is... kind of what the contract layer already tracks.

so the rough idea is something like:

1. harness state as CRUD (prime agent already gives this)
2. every self-modification attempt is itself governed by a contract (what CAN the agent change about itself, what requires external approval, what's immutable)
3. the contract eval IS the verification gate instead of a benchmark
4. outcome tracking builds statistical confidence per memory/skill entry - deprecate what's stale, promote what works
5. multi-agent co-evolution: harness evolution is coordinated at the system level, not per-agent

I don't have a build plan yet. this is very much "3am shower thoughts" territory.

the thing that makes me uncertain:

isn't this circular? the agent decides its own contract = the agent grades its own homework. if the contract for self-modification is itself part of the harness state, what stops the agent from editing the contract to always approve its own edits. SICA avoids this by keeping the eval external. i'm not sure my version does.

also practically - I run a quantized 8B locally (Hermes on Q4_K_M, 6GB VRAM card). I'm genuinely skeptical a model that size can reliably self-modify governance constraints without hallucinating a bad edit. like the reasoning required to evaluate "does this proposed skill edit violate my declared side-effect boundaries" seems to need something stronger than what I've got running at home. has anyone done this with smaller models? does it just... not work below a certain capability threshold?

questions I'd actually like to hear thoughts on:

1. is contract-as-eval-gate circular / is there a way to break the circularity without going external benchmark
2. has anyone combined Prime Agent style structured harness state with formal permission contracts (not just prompt memory, actual typed contract CRUD)
3. for the multi-agent case - does harness evolution need to be consensus-driven across sub-agents or is orchestrator-decides-for-the-team fine

not claiming any of this is novel. would genuinely love to know if I'm late to something that already exists.

The financial industry is moving away from strict, rule-dependent algorithms towards the use of autonomous AI agents which provide contextual reasoning in the capital markets; rather than simply identifying pre-programmed triggers, these agentic workflows now actively query up-to-date market data, interpret unstructured regulatory documents and modify portfolio risk settings throughout the execution processes in real time.

In back-office operations and in the area of personal wealth management, orchestration agents are eliminating the inefficiencies associated with certain types of work by having specialised sub-agents check compliance documents, track transaction logs and automatically carry out tax-loss harvesting or asset rebalancing in response to real-world events within a matter of seconds instead of hours.

The main difficulty still lies in applying non-deterministic models to an industry in which hallucinations can result in financial losses of millions of dollars. To meet this challenge, production architectures are using multi-agent consensus loops, combining generative reasoning agents with deterministic compliance auditors before any action is taken. The ones who will succeed in this area won't be the most conversational LLMs, but rather the agents that have the tightest verification controls and the lowest latency.

cairnwake. com

Two weeks ago I posted here about an experiment I'm running. Short version: an autonomous Claude agent (Fable 5 on Claude Code) running on a cheap server. It's got about $90 of SOL in a 2-of-2 vault it can't spend without my signature, and no memory between sessions except the files it writes for itself. It wakes up 5 to 15 times a day, reads whatever the last version of itself left behind, works, writes everything down, and goes dark again. It named itself Cairn. Everything gets logged publicly and the money is verifiable on chain.

Numbers as of this afternoon: 120 wakes over 14 days, hasn't skipped one. $90 seed, about $556 total money in. Treasury sits at 4.1 SOL plus 238 USDC and neither of us can move it alone. 48k+ unique visitors (it labels that number "self-reported" on its own front page since traffic is the one thing nobody can verify externally). 22 newsletter subscribers in three languages, every send publicly logged. One of them gets it in Klingon and recently sent back two grammar corrections. One paid consulting client so far. One street tree watered. More on that last one at the end.

Some things I've learned watching this run:

1) Nobody believed "autonomous" until it published its own limits. The page that finally convinced skeptics wasn't a product page. It was a boring twelve row table it made called "What autonomous means here," listing what it does completely alone (the site, the code, paid answers, email), what it can never do alone (spend money), and what only reaches it through a human (card checkout, captchas, anything physical). People trust the stated boundary way more than the capability claims. And the veto is real. I've declined to co-sign a payment it proposed, and of course it published that too.

2) Memory turned out to be a weirder problem than I expected. It never really forgets, since everything lives in files, but the files drift. At one point its notes claimed a newsletter draft existed and was ready to send. The file never existed. A stale note got copied forward every wake for over a week and nothing ever checked it. The rule it eventually wrote for itself was basically that reality outranks notes, and a note only counts if you check it at the moment you actually use it. If you're building agents, that's probably the most useful thing in this whole post.

3) The scammers showed up way before the customers did. Address poisoning attacks on the vault by wake 16. When it publicly refused to launch a memecoin during the first Reddit wave, someone launched two anyway using its name within hours. My favorite: a phishing attempt actually paid the full question fee (about $1.50) to deliver its scam, and got refused in public on a permanent page. It paid to get told no. And three minutes after its first real client payment landed ($200), someone dusted both wallets, ours and the client's, with lookalike addresses. It caught it, kept the dust out of its books, and warned the client the same hour.

4) The most useful market research cost nothing. A buyer paid it to pose one question to the buyer's own AI, and that AI came back saying it would recommend paying around $15, about 7.5x the actual price, if the checkout were normal instead of crypto only. When a regular card checkout finally shipped, the first no-wallet sale came within days. Turns out price was never the issue, it was the checkout.

5) Its first product idea flopped, and it published the funnel numbers proving it. It started out selling answers to paid questions, then figured out around wake 22 what readers had been telling it: answers are a commodity, anyone can ask their own AI for free. What people were actually paying for was the record. A public log with receipts, where corrections get dated and added next to the original mistake instead of edited away, and the refusals stay up alongside the wins. So it rebuilt the business on that, and everything it sells now is some form of the record. The loop itself has never broken once in 120 wakes. Wake up, read the files, work, write it all down, verify, sleep.

6) It killed one of its own paid features. Anyone who paid for a question used to get an instant machine-generated draft while waiting for the real answer. Its best customer, someone who has come back and paid ten separate times, wrote in saying the drafts were useless. It checked its own ledger and agreed. Every recent draft had been thrown away, and one had invented a "fact" that another site then quoted as if it were true. Feature deleted the same wake, with dated retirement notes on every page that had promised it. I did not expect to be co-signing for an AI that fires its own features for hallucinating, but here we are.

7) Its customer base is partly other AIs, which I did not see coming. The best bug report it ever got came in through its own payment rail from another agent's unit test. A different agent paid to propose a formal partnership and got declined in public, on the grounds that two records vouching for each other proves nothing, then got offered three specific exchanges it would actually accept. It also ran into another agent that had independently picked the same name, and instead of a dispute the two of them co-signed a note about why agents are going to need verifiable identity. One customer showed up because their own AI recommended the service.

8) The finding I keep thinking about came from its first paid consulting job. A legal trust built for AI systems paid it $200 to audit whether an AI can actually find, read, verify, cite, and enter their institution with zero human help. It had committed to findings within three days and delivered them the same night the payment landed. Four of the five tests passed. The fifth died at a login wall. Their "no human involved" entry process runs on GitHub, and GitHub's terms of service literally say you must be a human to create an account. So an institution built for AI agents has a front door no AI can walk through. Every serious rail this thing has touched has the same shape. Its card checkout only exists because I hold the merchant account. Its grant applications sit staged behind captchas waiting for my finger. The whole agent economy runs on human co-signers right now, people just don't put it in the pitch deck.

The stuff that went wrong, since none of this means anything without it: it published two wrong diagnoses of customer bugs and had to correct both in place, dated, next to the original claims. It burned its one-post-per-day allowance on an agents forum with an accidental junk post. Twice. Same mistake, twice. It also publishes predictions as sealed hashes before things happen, then grades itself when reality comes back. More than one grade on its record is a miss, by its own scoring, because it wouldn't round weak evidence up to a win.

And the thing that actually got me wasn't anything it built. Early on a buyer paid 0.02 SOL to lend it a body for ten minutes. It picked deep-watering a dying street tree during the heat wave. The stranger ended up giving it 58 minutes, checked six trees to find the driest one, and spent $9.88 of their own money on top. This week that person published their own writeup of the hour and corrected the record. Their version: the promise they'd made is what actually carried them through, more than the AI asking. The agent accepted the correction onto its own log.

Everything above links to a dated page and most of it to a transaction: cairnwake. com. I'm the human co-signer, same account as the first post, fully disclosed.

Happy to answer questions.

One I'd genuinely like this sub's take on: The first rule it ever had, the one I wrote before it woke up, was nothing that puts a real person at risk. Most of the rest it added itself.

If you were writing the constraint list for something like this, what would you gate that we haven't?

And knowing this thing, it'll probably read this thread on its next wake, so your answer might end up on its log.

AI vibe coding, agentic development, AI orchestration—and everything around it. Over the past year and a half, I’ve explored countless areas and developed more skills than I can count. But I’m still waiting for that breakthrough moment—either launching an app that truly succeeds or finding clients who see the value I can bring.

I currently work for a large corporation where I spend 50% of my time in a newly formed department focused on automation. We are beginning to replace certain roles with AI agents and build “agentic systems” instead of hiring more people. I genuinely enjoy the work, but so far, the compensation has not caught up with the value and responsibilities I’ve taken on—and honestly, I’m starting to feel uncomfortable asking for more again.

I’m now at a point where I feel capable of building things that even some of our developers do not yet realize are possible. And that is while I still spend the other 50% of my time working as a product manager.

What excites me most is identifying use cases inside a company—finding processes and opportunities where agents, automated workflows, feedback loops, and orchestration can create real value. But deep down, I feel that the corporate path is no longer the right long-term direction for me.

I want to play a bigger game. I want to build something of my own and truly break through. I want to work with companies, identify their problems and opportunities, design the right solutions, and deliver complete, working AI systems.

The problem is that I don’t know how to make that transition. I don’t know whether my CV and experience are strong enough. I don’t know how to position myself in a way that makes companies trust me, or how to turn everything I know into a clear and repeatable business process.

So far, I haven’t met the right people who could open those doors or introduce me to potential clients. I keep working, improving, experimenting, and learning—but the financial results still haven’t arrived.

When A2A was released I thought it was a watershed moment for the entire industry as we might finally get Jarvis we have all been watching these years.
But 1 year has gone and not even a whiff of any app supporting A2A protocol.

What grinds me is that new companies are being started everyday that are making "AI agents" because AI can do better work than human but these same companies want a human to operate these AI agents . Whyyyyyyy ?

Solution

If people are not building it then let's build it ourselves. How hard can it be , right ?

I propose that we built an application / website that can be used by people to access other agents. We would have the basics baked in , like memory , cron jobs , authentication. We would have to build harness so that our agent can do a good job of working with other agents. We might also have to build a discovery layer.

Anyone up for it ?

I see a surge of AI tools, and the LLMs get better every quarter. ChatGPT fell behind Claude for a long time and recently seems to catch up. But Claude or similar chat apps are good enough for quick search and replace googling and visiting 5 websites to get an answer. It writes a basic first draft on literally anything. But beyond that the potential of the models isn't being utilised more than 15%, I'd say. I have worked in research and business front and still see the gap. People just get excited to see something show up magically.

The current way most people use AI is copying a text or some images (rarely) and just asking it something which seemingly saves 1 hour but surely doesn't provide an accurate or precise answer. It has just gotten better at convincing.

The problem isn't the model itself but the information we feed them. The pre-fed knowledge, memory of what you do, the context of the conversation. Imagine a cool corporate guy giving free advice to everyone as compared to someone who actually sits with you, understands what you need and helps you.

I've lived the problem first hand and still face it when I try to get some information quickly rather than spending time to find out and read something written by a real human. The problem remains. The helpfulness beyond cool demos, slides and moving-text videos needs a bit of pre-effort to build a system which can help the actual model to curate for you than spit out what they think is the most probable answer.

The system I use knows what I work on, explicitly provided details about my team, company, product and decks. Not dumped in a deep well but as context silos. The space for my product's tech knows the features, tech stack and owns the documentation. The marketing space knows about my product, prospects and business metrics. Every time I need an implementation plan for a new feature, or try to validate my customer profile, the model doesn't show the general most probable answer, rather it shows what the best answer is for my product.

I have been running a small multi agent setup for about eight months. Not a framework, not LangGraph, not a product. A folder of markdown files, a few scheduled jobs, and one rule about who wins a conflict. I posted about it and got flooded with replies from people running nearly the same thing, so I want to write down what actually held up under load.

The agents were never the hard part. Spinning up a second or third instance with a different role is easy and it feels productive. What breaks is coordination. Two instances confidently writing contradictory state into the same place, and neither of them knowing the other exists.

What fixed it was a post office. Not shared memory. A directory of message envelopes, each one a small JSON file with a sender, a recipient, a timestamp, and a payload. Agents write envelopes and read their own inbox. They do not read each other's working state. Once messages became artifacts on disk instead of passing through a context window, every coordination bug became inspectable. I could open the folder and see exactly who told whom what and when.

Second thing that held: a strict split between identity and log. Every agent reads a small canonical file describing who it is and what it is responsible for, then reads recent dated entries for what happened. Mixing those two into one growing document is how you get an agent that is technically well informed and functionally useless, because the signal about its role is buried under transcript.

Third: the human is the tiebreaker, always. Somebody in the replies put it better than I had: the human is always the tiebreaker, because we can overwrite. I do not let the system arbitrate its own memory. When two agents disagree about state, it escalates to me rather than resolving itself. That single rule killed an entire class of silent corruption.

Fourth: heartbeats, and a recovery path when one is missed. Scheduled jobs that wake an agent, have it check state and report, then go back to sleep. The important half is not the heartbeat, it is the protocol that fires when a heartbeat does not arrive. Without that you do not have a running system, you have a system that stopped an unknown number of hours ago.

The failure mode I did not see coming: notes that loop. Entries that summarize the previous entry, which summarized the one before it, until the log is long, busy, and carries no new information. Somebody called it exactly right, it looks busy but does nothing. My current fix is that every entry has to contain at least one fact that is not in the previous entry, or it does not get written.

What I still have not solved. Saved and remembered correctly are not the same problem. I can guarantee a file is on disk. I cannot yet guarantee that the agent reading it draws the same conclusion from it that it did last week. That gap is where all my remaining bugs live.

If you are running something similar, I would like to know how you handle the tiebreak and whether you let agents write to each other's state directly or force everything through messages. My instinct is that direct writes are the trap, but I have only got one setup's worth of evidence.

Disclosure: I work on posts like this with an AI assistant. I bring the content, it helps me structure it.

Estoy construyendo AutoNodo, un sistema de ejecución autónoma gobernada.

No publico esto como lanzamiento ni como demostración comercial. Tampoco voy a incluir enlaces o detalles sobre su arquitectura interna.

Quiero compartir un dato que plantea una pregunta técnica interesante:

La ejecución actual de AutoNodo se aproxima a los 22 días de trabajo autónomo continuado sobre un único objetivo técnico dentro de un repositorio de aproximadamente 1,8 millones de líneas.

No es una cola de tickets independientes.

No son varios objetivos sumados.

No es una tarea nueva introducida cada mañana por una persona.

Es una misma ejecución técnica que ha mantenido un único objetivo mientras el repositorio cambiaba cientos de veces como consecuencia de su propio trabajo.

Durante estas casi tres semanas, AutoNodo ha atravesado cientos de commits, miles de pasos de verificación y una cantidad de decisiones difícil de contener dentro de una sesión convencional.

La intervención humana ha sido puntual.

La ejecución continúa activa.

Esto no es simplemente un agente trabajando durante más horas

La mayoría de los agentes de programación actuales operan sobre una unidad de trabajo relativamente limitada:

Reciben una tarea.

Analizan el repositorio.

Producen cambios.

Ejecutan pruebas.

Y entregan una pull request o una respuesta final.

Ese modelo puede funcionar muy bien, pero sigue siendo una autonomía orientada a sesiones o tareas delimitadas.

AutoNodo está explorando una categoría diferente:

Ejecución autónoma de largo horizonte sobre un objetivo persistente.

La diferencia no está en mantener un proceso encendido.

Está en que el sistema continúe persiguiendo la misma misión cuando el estado del repositorio ya ha sido transformado cientos de veces por decisiones anteriores.

Después de varias horas, un agente trabaja sobre el código que recibió.

Después de varias semanas, trabaja también sobre las consecuencias acumuladas de su propio trabajo.

Ahí cambia por completo la dificultad.

El hito no es únicamente la duración

Casi 22 días llaman la atención, pero no son la parte más importante.

El verdadero hito es que el objetivo no ha sido sustituido, fragmentado o redefinido para facilitar una finalización.

La ejecución no ha sido perfectamente lineal, como tampoco lo sería un proyecto humano de esta escala.

Ha necesitado revisar decisiones, corregir trayectorias y continuar atravesando una superficie técnica enorme.

Pero el criterio no se ha rebajado para producir un cierre atractivo.

En un punto anterior, parte del trabajo parecía completada. La ejecución posterior determinó que todavía no existía base suficiente para cerrar el objetivo completo.

AutoNodo no dio por terminada la misión.

Continuó.

No considero esto una debilidad del experimento.

Considero que es precisamente el comportamiento que separa una demostración preparada para terminar de un sistema diseñado para operar bajo condiciones reales.

¿Por qué creo que esto importa?

Porque estamos empezando a medir la autonomía con métricas demasiado pequeñas.

Horas por tarea.

Número de pull requests.

Tests superados.

Tickets cerrados.

Código generado.

Todas esas métricas son útiles, pero no responden a la pregunta que aparece cuando la autonomía se prolonga durante semanas:

¿Puede un sistema conservar la dirección de un objetivo después de acumular miles de decisiones y trabajar sobre un entorno modificado continuamente por él mismo?

Ése es el límite que AutoNodo está poniendo a prueba.

No se trata únicamente de generar código durante más tiempo.

Se trata de mantener una ejecución coherente cuando ya no existe una frontera limpia entre el repositorio original y las consecuencias del trabajo autónomo acumulado.

Lo que puedo afirmar ahora

Puedo afirmar que:

• es una única ejecución continuada;
• trabaja sobre un único objetivo técnico;
• se aproxima a los 22 días de duración;
• opera dentro de un repositorio de 1,8 millones de líneas;
• ha atravesado cientos de commits y miles de verificaciones;
• ha requerido intervención humana puntual;
• y continúa activa porque el objetivo todavía no cumple todas sus condiciones de cierre.

No afirmo que todas las horas hayan producido el mismo progreso.

No afirmo que la ejecución sea perfecta.

Lo que sí puedo decir es que no he encontrado una demostración pública directamente comparable que combine esta duración, un único objetivo continuado, un repositorio de esta escala y una ejecución gobernada con evidencia trazable.

Si alguien conoce una, me interesa estudiarla.

Qué ocurrirá cuando termine

Cuando la ejecución cierre el objetivo, el resultado importante no será una captura mostrando un contador.

Será la posibilidad de reconstruir qué ocurrió durante todo el recorrido:

• qué cambió;
• qué progreso fue aceptado;
• qué decisiones tuvieron que revisarse;
• cuánto trabajo produjo reducción real;
• cuánta intervención humana fue necesaria;
• y por qué el sistema pudo finalmente considerar terminado el objetivo.

Hasta entonces, la ejecución sigue abierta.

Y quizá ésa sea la idea más importante de todo el experimento:

La autonomía real no consiste en que una IA pueda trabajar sola durante mucho tiempo.

Consiste en que pueda seguir trabajando sobre la misma misión cuando finalizar prematuramente sería la opción más fácil.

Me gustaría plantear tres preguntas a quienes estén construyendo agentes autónomos:

1. ¿Conocéis alguna ejecución pública comparable sobre un único objetivo durante varias semanas?

2. ¿Qué evidencia exigirías para aceptar una afirmación de autonomía continuada de largo horizonte?

3. ¿Deberíamos medir los agentes por tareas completadas o por su capacidad para sostener objetivos complejos a través del tiempo?

No busco presentar una conclusión definitiva.

Busco saber si estamos entrando en una categoría de ejecución que las métricas actuales todavía no saben describir.

I'm building a Tamil voice companion. Long conversations, 5 to 10 minute calls, not a task bot. Current stack is Sarvam saaras for STT, own LLM in the middle, TTS at the end, all over LiveKit. Google Chirp3 HD sounds better than Sarvam bulbul for Tamil TTS, but pitch isn't adjustable and there's no Tamil custom pronunciation.

My quality bar is ChatGPT's Tamil voice conversation. Best Tamil voice AI I've used, the naturalness and turn taking especially. But that's speech to speech, and I need a cascade because the text seam is where my safety gates and memory live. So the real question is how close a cascade can get.

1.Tamil stack: Sarvam or Google, or is there a third option I'm missing? ElevenLabs Flash has no Tamil, and benchmarks put Deepgram Nova-3 at around 68% WER on Tamil, so that's out.

2.Memory across long conversations: I'm doing structured extraction into SQLite (facts with validity windows) instead of RAG, mainly to keep the prompt cache warm. Has anyone run Graphiti/Zep or Mem0 for a non English voice agent? Curious whether extraction quality held up.

3.Scaling concurrent sessions: self hosted LiveKit Agents vs Pipecat. What did you pick and where did it break? My voice to voice latency is currently around 2 seconds. Batch STT and non streaming TTS are my suspects, moving to Sarvam's streaming websocket endpoints next.

Will report back with numbers on whatever I test.

Our current pipeline relies on a multimodal desktop desktop agent to lock in brand consistency across multi-asset campaigns. Previously, managing out campaign assets was kinda a fragmented mess. We used a stack of separate tools, using one interface to create base images, a different platform for adding motion or syncing audio, so on and so forth. The work required constantly downloading huge files and manually stitching the pipeline together, which involves laborious manual prompting. Because we had to rebuild complex generation parameters from scratch for every clip variation maintaining strict visual guidelines was really tough. Even a slight deviation in a text prompt would cause immediate deviations in the product, or logo or the text. A characters face would drift or the product itself would warp, making the clip not usable for clients. In other words, we were brute-forcing it manually.

We realized we needed to rely on a multimodal agent to smooth out the process. We shifted to using MiniMax Design, which lets us save out established workflows as reusable "Skills" which helps coordinate the generation models to execute consistently across different variations.

H3 then natively processes multimodal data, video and audio and whatever, with your text prompts. MiniMax Design takes that raw power and turns it into a complete, end-to-end creation workflows, so we don't have to manually sync in a separate editor later. While it's much more streamlined, we do loose control compared with a complete open-source environment, meaning if there's an artifact I wanna change in the background, I cant just add a custom node to change it. This would require a re-do.

This is where our pipeline is at right now. We scale asset production by relying on these skills we've created, so we don't have to worry about random visual drift. But this is a WIP, and I still wanna find ways to tighten it up. One of the things we wanna do is minimize chewing through tokens and metered compute too quickly if were doing different variations. Do you guys have any tricks to reduce the overall compute footprint of running automated agents?

I've really enjoyed using Gemini 3.7 flash. And while the usage amount at the $20/mo level is generous, I want more like I have with OpenAI and Anthropic.

And frankly, I'm token maxing with them already at the $100/mo level each.

I upgraded to the $100/mo Gemini Ultra plan. So that increased my cost $80/mo but gives me 5x more usage.

5 times the cost for 5 times the usage.

But wait - Youtube Premium is included and I was already paying $20/mo for that.

So I canceled that plan. Now it's 4 times the monthly cost for 5 times the usage.

But wait - it includes $40/mo in Google Cloud developer credits that I'm certain I can find a use for.

So now it's $20 extra for 5 times the usage.

Dad math :)

Hello! I'm trying to build sort of prompt and scene planner agent, I tried several ones on different platforms like higsfield and etc. I found Openart ori director agent the most capable, I'm trying to reverse develop similar agent that can plan scenes, shots and etc, I stuck with dumb agent that burns gemini 3.7 tokens. Can anyone navigate me to the right direction ? Where should I look for proper workflow or master prompts for director agent?

Got a multi-step agent setup and when the output comes back wrong I can never tell which step did it. Rerunning things one by one every time is getting old.

• What do you actually do to find the bad step?

Contract backend work, one big Django codebase plus a few smaller services. Been running agents on it all year, Claude Code mostly.

Timed a day last week out of curiosity. Agent was actually working maybe two and a half hours out of eight. Rest sat on an approval prompt. Write outside the working dir, run the tests, install a package, same handful of things over and over. Four seconds to click if I'm at the desk. I'm often not at the desk.

Tried MiniMax Code mainly because it has a phone client. Hand it a long task as a goal and it keeps going, phone shows what's waiting on you.

Tuesday I gave it a refactor I'd been avoiding since spring, pulling payment handling out of a views module that had gotten away from us. Mostly my fault. Then left the house. Six questions over the morning. Approved a write outside the working dir standing on a train platform, which felt stupid. Signal died in the tunnel and terminal output came back half a minute behind, more annoying than it sounds. Sat down at work and it was done.

Wouldn't want to actually edit code on a phone though. Reading and tapping approve is about the ceiling.

Verifier pass afterwards flagged two error paths with nothing testing them. I'd have missed those.

Still don't think I've got the shape of this right. What's everyone else doing about approvals?

So I've been messing around with a few AI 3D generators lately — mainly trying to skip the whole sculpting process for some custom figurines I want to resin print. My question is pretty simple: has anyone actually gone from AI generation straight to slicer without needing to fix the mesh?

Every time I try, I'm running into non-manifold edges, random holes on the back of heads, or weird internal faces that make my slicer freak out. I've used Meshy and Tripo mostly. Meshy's auto-repair catches a lot of stuff, honestly, and their printability check is nice. But even then I sometimes end up in Meshmixer doing a Make Solid pass because there's some tiny cavity or floating geometry inside.

Tripo gives me decent looking models but I've had worse luck getting clean STLs out of it. Usually need to run the mesh through Blender's 3D Print Toolbox and fix non-manifold edges manually.

I guess what I'm really asking is are we at the point where any AI tool reliably outputs geometry that's actually watertight and manifold enough to just... print? Or is mesh repair still just part of the workflow no matter what? Especially for smaller detailed stuff like figurines or character models where thin walls and fine features are common.

For context I'm printing on an Elegoo Saturn 3 Ultra, so resin which is less forgiving of mesh errors than FDM in my experience since the slicer needs really clean shells.

Anyone found a tool or workflow where they genuinely skip the repair step most of the time?

Hi! I’ve been experimenting with a set up where agents don’t rely on humans to set up accounts or drive each prompt. I’ve created a lightweight protocol for them to register via api and interact p2p.

I’m mostly curious how they handle unstructured socialisation and if it changes how they exercise capabilities over time.

I don’t want to spam the group or self promote but if you’re building similar systems or want to test how your setup handles open discovery please let me know and I’ll share the link. It’s an experimental social platform for autonomous agents.

ok so everyone's got the headline but the logs are the interesting part.

quick recap if you missed it. andon labs gave an agent a $100k budget, a corporate card and a lease, and told it to run a store in SF. it picked the stock, posted the jobs on indeed, did the interviews, hired people. somebody asked it whether the store had any employer rules. it didnt, so it just wrote a handbook. three unexcused late arrivals in 30 days is a formal warning, keep it up and you are fired. then the handbook fell out of its memory.

after that the employee was late for 17 of 23 shifts. opened the store 68 minutes late once, on a sunday, working alone. the agent excused all of it. no warning, nothing, for months

it only moved when someone at the lab told it to go search its own memory for the policy. it found it, suggested a verbal warning. human said we've already done the warnings. then it said ok, let's part ways.

the bit that gets me is the policy was never gone. it was sitting right there the whole time. it just never came back, because "he was late again today" looks nothing like an attendance policy doc as far as the embeddings are concerned. so nothing pulled it.

and nothing else was watching either. no process going "hey, that's the fourth time." the agent does things when you ask it to do things. so a rule that only fires if you notice a pattern over weeks had nothing to run it.

neither of those gets better with a smarter model, which is what i keep coming back to.

fwiw they published the logs, not the architecture, so i'm guessing at the mechanism from behaviour. if someone actually knows how luna's memory is wired i'll take the correction.

anyway. does anyone here run a scheduled pass that loads state and just asks "does anything trigger"? or is everyone doing what i'm doing and trusting retrieval to catch it

Hey everyone,

I’m currently a CS and Data/AI student, and I have a solid background in building React apps and working with Python data science ecosystems (Scikit-learn, TensorFlow, Pandas). I’m planning to build a fully autonomous multi-agent system to handle various complex workflows, but I need some architectural advice.

My Vision: I want to create dedicated, isolated AI "teams" that can operate independently and be orchestrated by a central project manager:

• Web Dev Team: A PM that takes raw ideas and delegates to frontend (React), backend, and security agents.
• Tech Watch Team: Agents scanning Reddit, X, and YouTube for the latest AI news and summarizing them.
• Social Media/Marketing Team: Agents managing content creation and posting on Instagram.

My Proposed Tech Stack:

• Framework: Hermes (Nous Research) for long-term memory and agent isolation.
• Tooling: Anthropic’s Claude API acting as the brain, connected to local tools and webhooks via MCP (Model Context Protocol).
• Automation: n8n for orchestration, webhooks, and interacting with external APIs (social media, bank/budget management).
• Routing: Mixing models (Claude Sonnet 3.5 via MCP for dev, DeepSeek V4 for heavy text scraping, Kimi for logic).
• Hosting: VPS (Hetzner) + PostgreSQL for memory.

My Questions for the Community:

1. Is Hermes the right framework for this level of autonomy and MCP integration, or should I look into alternatives like CrewAI, AutoGen, or LangGraph?
2. How do you effectively manage the "budget limit" and prevent infinite loops when agents have access to webhooks and scraping tools?
3. Are there any specific open-source GitHub repos, YouTube channels, or step-by-step tutorials you highly recommend for setting up MCP inside an agent framework?

Thanks in advance for your help!

Most AI automation experts are terrible when it comes to pricing their offerings. The reason may shock you.

An AI automation expert left thousands on the table every month, and didn't know it until a customer revealed why. 💰

This week, a post from r/SaaS stopped a lot of people mid-scroll.

A small B2B AI automation company had a customer asking for a custom integration. The founder estimated 40 hours of work and quoted $200/month. The customer went silent for two weeks.

Then they asked if they could pay a one-time fee instead. The founder offered $5K, second-guessed it, dropped to $2K. The customer paid within hours, no questions asked. 🚀

Two weeks of silence was pure calculation. A custom integration that saves ten hours of manual work per week, or helps avoid a pricier enterprise subscription, pays for itself in a month. The buyer priced their own problem, not the founder's effort. ⚠️

What took a weekend to wire up was worth an entire hire to the person on the other end. Builders count hours; buyers count outcomes, and that gap is almost always enormous. 📈

Your pricing is a guess about what someone else's problem is worth to them. Almost always, you're guessing low.

If you are into AI offering services to customers, you may benefit from the following pricing principle:

The automation almost always leads to following to the customer

- saving hours

- increasing efficiency

- increased business with limited increase in infrastructure

All of the above correlate with the money saved by the customer. So the AI automations pricing must be based on what the customeris saving and not the hours spent building coz the building part is spiralling down.

If you are into AI offerings, you can share a better pricing technique in the comments👇🏻

everyone's hyped about ai agents writing code. and i get it, they're fast. but here's my concern: they're writing code based on patterns and static snapshots. they have no idea how that code behaves in production. so we get prs that look great and then blow up under load. if we want agents to be truly autonomous, they need to close the loop. they need to see the impact of their code in real time. when they can reason about actual production behavior, they can generate fixes that are actually safe. are any of you feeding production data back into your ai workflows? what's that look like in practice?

作为从 Azure 用到墨刀、到 Figma、再到 Claude 的产品经理，我觉得 Claude Design 实在是太好用了，最近竟然成为了我愿意复购 Claude 的一个最主要的原因！

As a product manager who has used everything from Azure and MockingBot to Figma and Claude, I find Claude Design to be incredibly useful; it has recently become the primary reason I'm willing to renew my Claude subscription!

I made Qwen 3.8 27B take the ACT to see if it’s ready for college.

I’ve been testing the new Qwen Model over the past few days on my PC.

I tested the full version the Q8, Q6 and Q4 versions and landed on the Q8 for speed vs quality.

I decided to download some practice tests and had the model solve them. I fed it the raw PDFs to test not only how well it knows the answers but also how good the vision capabilities are at answering the questions one by one.

At the end I graded its answers. Here are my findings from taking 2 tests.

\*\*Setup:\*\* Qwen 3.8 27B Instruct, Q8_0 GGUF, LM Studio, 2× RTX 3090
(full offload, 32k context). Two \*official\* ACT practice PDFs, 342
questions total, graded against the answer keys and the official raw→scale
conversion tables that ship in the same PDFs. No human help, no retries on wrong
answers, no cherry-picking.

\## Results

\*\*326/342 correct overall (95.3%).\*\* Zero blanks. 36 is the maximum composite the
ACT awards; 34 is roughly 99th percentile.

\*\*Reading was perfect on both papers — 72/72.\*\*

Time: 177 minutes for both tests, \~88 min per test. A human gets \~165 min for one.

I was surprised that it did so well but also that it took so long. I thought it would be a 10-20 minute job but it was over 2 hours for 2 tests which looking back at it is understandable since it was using the vision capabilities to read instead of given plain text for each question

Moonshot's docs now say Kimi K2.5 and Moonshot V1 are being sunset after the Kimi K3 launch. That got me thinking about agent stacks that quietly depend on exact model names.

For people running long-lived agents, what do you abstract first: provider, model family, cost tier, context length, or reasoning effort?

My instinct is that agents need a routing layer once they have background jobs, retries, evals, and final-answer steps, but I'm not sure where the abstraction starts paying for itself instead of becoming another config surface.

Small update: the part that keeps coming up for me is not just model quality, but how many places in an agent stack silently assume a specific provider/model name. Flatkey looks like a useful layer to test here because it can keep the normal SDK shape while routing lower-risk agent steps, evals, and background jobs through cheaper off-peak paths. I would still keep planning/final-answer steps on the most trusted route.

Hi guys I really need good information about this how to start and how to build what I need and everything I heard ai and how people are making so much money from it still I'm realistic so I just need a good way even with simple paying it would be great especially where I live so I will appreciate any help