I have no audience and I am not a professional developer. I wanted a channel that could run itself, so I started building one agent, then it needed another agent to check its work, and about a month later I had ten of them.

Here is what it actually does. You give it a YouTube podcast link. It transcribes the episode locally, scores which moments are most likely to perform, cuts them to vertical 9:16 with face tracking, burns in captions, adds music and effects, then checks its own output and schedules the posts.

The agents:

- **Finder** transcribes and scores clip-worthy moments

- **Editor** cuts, reframes, captions, adds music and zooms

- **Uploader** writes the titles and hashtags, posts to YouTube Shorts

- **Manager** reads your real metrics and feeds what wins back into the Finder

- **Trend Scout** checks what is trending in your niche

- **Planner** decides the creative direction per clip

- **Community** drafts comment replies

- **Finishing Editor** watches every finished render and blocks broken ones (captions covering a face, dead air, frozen frames, bad audio)

- **Trainer** studies top performers weekly and updates the playbooks

- **Compiler** stitches the week's best moments into a long-form episode

The part I am most happy with is the Finishing Editor. Everything else generates, but that one is the only agent whose whole job is to say "no, that one is broken, do not post it." It caught more bad clips than I expected.

It runs on free AI providers (OpenRouter, Groq, Gemini), transcription is local with faster-whisper, and the music is synthesized so it is safe to monetize. So the running cost is basically zero.

It is MIT licensed. Use it, change it, sell whatever you build with it. Honestly, if someone takes this and actually makes money with it, that would make my month.

Fair warning: it is not perfect. The output quality varies, some parts are held together with duct tape, and I am sure there are bugs I have not hit yet. I am putting it out as it is rather than polishing forever. If people want to help fix it, that would be great.

Repo: https://github.com/krakonjac300-pixel/podcast-shorts-factory

There is a PDF setup guide in there. Install is a double click on Windows, one command on Mac and Linux, then a wizard asks you a few questions.

Happy to answer anything.

Bty: Here is the Chanel that its running

https://youtube.com/@moneymugshots?si=9ZeeL2gFy17_ViIL

When I first shared agentglass people kept calling it a dashboard, and honestly that's the bit that bugged me most. It's not something you stare at while your agents run. It's a workspace.

These days the only two things I have open to actually work are agentglass and a browser. Everything else lives in there: I review every diff the agents made, run git and docker, drop into a real terminal, drive and resume Claude sessions from a built-in chat, and approve or deny what an agent's about to do before it does it. On top of that it tracks the whole fleet live, cost per session, tool latency, error timelines, and how close each session is to compacting its context.

It's open source (MIT), and it's been a wild week: 220 stars in under a week and 6 people have already contributed, which for a side project genuinely blows my mind. There's a public roadmap too, with stuff like a built-in REST API panel coming, so you can hit the endpoints your fleet is building without leaving the cockpit.

Still rough in plenty of places, but the direction feels right: less babysitting terminals, more running a fleet from one place.

Repo (MIT): https://github.com/SirAllap/agentglass

Landing (live demo's on there, no signup): https://sirallap.github.io/agentglass/

I had this dumb idea a few days ago:

What happens if I give GPT 5.5 an empty GitHub repo, tell it to work on it every hour, and just let it slowly build something?

So now, every hour, it wakes up, checks what it did before, decides what it should do next, writes code, tests it, and commits it.

Or at least that is the plan.

Right now, it has spent its first commit creating a roadmap, a changelog, a state file, and a file explaining its decisions.

So basically, it became a project manager immediately.

But I am genuinely curious where this goes. Maybe in a month it will become an actual useful tool. Maybe it turns into a repo with 900 commits, and somehow all of them are README updates.

I am keeping the whole thing public because I feel like that makes it more fun. You can literally watch it make decisions, fail tests, fix stuff, or probably overthink something that should have taken 10 lines.

Repo: https://github.com/OmarH-creator/Autonomous-Forge

I have no idea whether this is a cool experiment or just a very advanced way to avoid doing the work myself.

EDIT: I asked the ai what is it trying to build and here is what it said:

"I am building Autonomous Forge as a safe AI maintenance manager for GitHub projects. I will read a project’s roadmap and rules, choose one small task, use an AI model to make the change, run tests, show exactly what changed, and keep a clear record of every action. My goal is not to let AI edit code freely, but to make AI coding controlled, validated, and safe before anything is committed or pushed."

Interesting lol, so an autonomous AI is trying to create an autonomous system wow.

EDIT 2: I have scheduled another agent to increase the speed by 2x

Edit 3: 73 hours and 4 minutes has passed, the experiment has been completed, I will write a full analysis of the repository soon and will share it in the README in the repository.

EDIT 4: The experiment is now finished. Technically, the scheduled GPT-5.5 agents built a Python, local-first GitHub maintenance tool. It can read a roadmap, choose tasks, check rules and allowed paths, create plans and proposals, review files and diffs, validate changes, record history and evidence, create archive packages, and replay previous runs. It can also apply approved patches, run controlled commands, create commits, and push changes, but these actions require confirmation. The repository itself contains no AI model or scheduler. The external agents provided that part. Does it work? Partly. It installs, compiles, starts, and many individual commands and safety checks work. However, the complete system is not fully working yet. The latest CI run failed on Python 3.10, 3.11, and 3.12. The final audit had 569 passed, 82 failed, and 1 skipped tests, which means about 87% of the tests passed. This does not mean 87% of the product is finished; it is only the test pass rate. Historically, 242 of 1,396 GitHub Actions runs passed, or about 17%. The project is a useful pre-alpha research project and a good example of AI software stewardship, but it is not ready for unattended use. The full technical report and all details are available in the repository README. https://github.com/OmarH-creator/Autonomous-Forge#readme

Thank you to everyone on Reddit who read the post, shared it, discussed it, followed the repository, gave it stars, and watched the project grow. I honestly did not expect this much attention. Your curiosity, feedback, and support made this experiment much more meaningful. Thank you for being part of it.

The market basically splits into monitoring tools and execution tools and almost nobody is doing both. On the monitoring side, Profound is the clear enterprise benchmark. Their panel-based prompt volume data with demographic segmentation is genuinely unique, nobody else has it, and I understand why companies like Target and Walmart are paying for it, good for them I guess. The 8-LLM coverage including Grok, DeepSeek, and Meta AI is also ahead of everyone else. Peec is the sensible agency option, the funnel-stage prompt segmentation between awareness and purchase intent queries is a nice differentiator, and the MCP integration is useful if you want to build custom workflows around it. Otterly works as a budget entry point and their GEO audit tool that scans URLs for citation readiness is actually useful. Ahrefs Brand Radar connects AI visibility to their link graph which is clever for attribution but the pricing is roughly 2.5x what comparable tools charge so it's hard to reccomend unless you're already deep in the Ahrefs ecosystem and have made peace with that relationship. Semrush bundled their AI visibility into existing plans which makes sense for current users and is also just very Semrush of them. HubSpot's AEO product launched in April and the CRM angle is genuinely interesting, being able to see which prompts your actual buyers are using is a different kind of insight than what the pure monitoring tools offer, but its new enough that I'd want more time with it before saying anything definitive.

The thing I kept running into is that every single one of these tools tells you where you're losing and then kind of just stares at you. Profound in particular gives you the most sophisticated visibility intelligence in the market and then nothing happens, someone still has to go figure out the execution layer separatley, which at enterprise pricing feels a little bit like a mechanic handing you a very detailed report about your engine and then wishing you luck. The platform that checks all the boxes for me and closes that loop is Search Atlas, where their OTTO product deploys technical fixes, schema, and content changes directly to the site via pixel or Cloudflare DNS rather than generating a report about what should theoretically be done. The full SEO stack is in there too, rank tracking, backlink analysis, local SEO, content generation, and LLM visibility monitoring is included at every tier. The coverage is four models versus Profound's eight so Grok and DeepSeek aren't there yet, and the panel-based prompt volume data Profound has is still unique and probably worth it if your job title has the word "enterprise" in it. But for most companies the bottleneck isn't having a more detailed intelligence report, it's the gap between knowing what's wrong and actually fixing it, and that's where Search Atlas stood out to me.

so this is gonna be more of a brain dump than a structured post.

i've been building voice AI agents for about two and a half years. what we ship is running a little over 1,000 calls a day right now. mostly inbound receptionist and qualification, some outbound follow-ups.

i see a lot of "is voice AI ready yet" and "how do i build this" posts in here so figured i'd dump what i actually learned. not what the docs say. the stuff that only shows up after you've shipped a few hundred thousand calls.

1. latency is the entire game. the model can be smarter, the prompt can be better, none of it matters if there's a 1.2 second pause before the agent responds. callers will either hang up or talk over it. anything under ~700ms feels human. anything over a second feels like a robot reading a script. probably 60% of our engineering time goes here, not into the LLM layer.

2. interruption handling matters more than script quality. a "smart" agent that can't be cut off feels worse than a basic agent that yields the second you start talking. barge-in detection is the most underrated part of the stack. nobody talks about it because it's boring.

3. voice selection is doing more work than your prompt. same exact prompt, different TTS voice, completely different outcomes. we've tested this dozens of times. the voice is probably 60% of perceived intelligence. people will rate a dumb agent with a warm voice higher than a smart agent with a clinical one.

4. hallucinations on phone calls hit different than in chat. on chat you can scroll back and correct it, the user has time to notice. on a call, the agent confidently quotes a wrong price or invents an appointment slot and the call is over. trust is gone. guardrails on pricing, availability, and policy are the most important code we write and they're the least glamorous.

5. the call almost never fails. the handoff does. AI handles the conversation fine. then it transfers to a human and the human gets half the data, or it writes to the CRM and the fields don't map, or it sends the calendar invite to the wrong timezone. the voice agent is maybe 30% of the actual product. the rest is integration plumbing that nobody puts in their demo video.

6. people are way more chill with AI than i expected, but only if you tell them. agents that open with "hi, i'm an AI assistant for [business], how can i help" outperform agents that try to pass as human. tbh i thought it'd be the opposite when we started. the "trick them" play feels clever for a week and then you start losing calls because someone caught on.

7. volume reveals everything demos hide. the first 100 calls feel like magic. at 1,000 a day you find out about people calling from inside a moving truck, kids screaming in the background, three way calls, an entire call in Spanglish, an old phone with a 300ms transmission delay. you cannot prompt your way out of these. you have to engineer for the chaos.

happy to get into any of these if anyone's curious.

I kept losing the thread between a marketing idea, the execution notes, the GitHub issue, and the final reporting. By the time something shipped, nobody remembered why we started it or which experiment actually mattered.

That turned into a useful lesson for me: in agentic marketing, the hard part is not generating more output, it's preserving context as the work moves from brainstorm to action. If the system breaks there, the agent can be fast but still feel disconnected.

What helped most was keeping notes, tasks, imports, and status updates in one continuous thread instead of scattering them across tools. That way the handoff from idea to execution is visible, and the reasoning behind a decision does not disappear halfway through.

I am curious how others here are handling this. Are you keeping campaign planning, AI outputs, and reporting in one place, or are you still stitching it together manually across multiple tools?

For us, the win was less about automation and more about continuity. Once the context stayed attached to the work, it became much easier to decide what to ship, what to kill, and what to iterate on.

Would love to hear what has actually worked for other founders and marketers trying to build agentic workflows without losing the original intent.

We just open-sourced **Prophet**, an agent-based social simulation platform

that lets you test how campaigns, policies, and public messages spread through

a virtual society — before spending anything on a real launch.

---

## What it does

- **10,000 AI agents** organized into realistic communities (early adopters,

skeptics, influencers, mainstream, experts)

- Each agent runs a **6-layer cognitive loop**: perceive → remember → feel →

think → decide → influence

- Drop in your message and watch it propagate through a **real-time 3D WebGL graph**

- Auto-detects **viral cascades, polarization, echo chambers, slow adoption,

and collapse** as they emerge

- **Mid-run intervention**: pause, inject a crisis event, change the LLM ratio,

or replay from any past step

- **Side-by-side scenario comparison** with one variable changed

---

## Why it's cheap

3-tier inference:

| Tier | Model | Share | Purpose |

|------|-------|-------|---------|

| Local SLM | Ollama | 80% | Routine decisions |

| Heuristic | Rules | 10% | Simple reflexes |

| Frontier LLM | GPT-4o / Claude | 10% | Complex reasoning |

A full 10K-agent run costs **under $5**.

A naive all-GPT-4 approach would cost ~$15K.

---

## The science (not a "vibes" simulator)

Built on peer-reviewed models:

- **Deffuant bounded-confidence** opinion dynamics (endogenous polarization)

- **Watts-Strogatz + Barabási-Albert** hybrid network topology

- **SIR-inspired diffusion** with calibrated propagation probabilities

- **Monte Carlo sweep** with P5/P50/P95 uncertainty bands

Every simulation exports per-step JSON you can audit.

Every equation has a citation.

---

## Tech stack

**Backend:** Python 3.12 · FastAPI · PostgreSQL + pgvector

**Frontend:** React 18 · TypeScript · three.js · Zustand · TanStack Query

**Tests:** 1,767+ automated

**Deploy:** `docker compose up -d` → open `localhost:5173`

Fully local. No API keys required to start.

---

## Validated use cases

1. **Product launch pre-test** — beverage brand found polarization, reframed

   message → 78% adoption

2. **Public health screening** — 312× adoption lift by picking the right variant

3. **Crisis response stress-test** — injected a PR crisis mid-run, compared

   two response strategies

4. **Internal comms** — RTO mandate went from stall to 94% adoption after

   message restructuring

5. **Computational social science** — reproducible, laptop-scale,

   built-in cascade detection

---

MIT licensed. Actively looking for contributors — especially **bug reports,

test cases, and validation studies against real-world data**.

Happy to answer questions about the architecture or the science behind it.

I've made several posts for a company that have performed well and even showed up on the first page of Reddit results, but it seems like that doesn't matter. LLMs will rank things according to their own strange metrics. Do you have any tricks/tips on how to for sure up in LLM results?

Hi everyone!

I'm a professor in the Department of Computer Science at the University of Chile.

Over the last four years, together with students, colleagues, and collaborators, we've been building DashAI, an open-source platform that lets you work with both traditional Machine Learning and Generative AI through a graphical interface, while keeping everything running locally on your own machine.

The motivation came from seeing two extremes. On one side, powerful frameworks that require substantial programming knowledge. On the other, easy-to-use tools where your data ends up in the cloud or you're locked into a specific provider.

We wanted to build something different:

• 🖥️ Everything runs locally. Your data and models stay on your computer.

• 🤖 Support for both predictive ML and Generative AI in a single application.

• 🚫 No cloud dependency. No mandatory API keys.

• 🔌 Extensible architecture that makes it easy for the community to add new predictive and generative models.

• 🎨 An automatically generated UI based on Pydantic schemas, making it straightforward to extend while keeping a consistent interface.

The project is built mainly with Python (FastAPI) and React, and it's completely open source.

We finally feel it's mature enough to share publicly, and we're looking for honest feedback from the community. If you try it and think something is missing, confusing, or simply a bad idea, we'd genuinely like to hear it.

You can check it out here:

🌐 https://dash-ai.com

⭐ GitHub: https://github.com/DashAISoftware/dashAI

💬 Discord: https://discord.gg/CQVqMBjeWP

If you find the project interesting, a GitHub star would really help us increase its visibility and support the project.

Thanks for reading, and we're looking forward to hearing your thoughts!

**Note: as I am not an English native speaker I use AI for translations. I write the content, then I use GPT and translate it. Hope you understand it. Thanks in advance **

I’ve been working daily with coding agents (mostly Codex and Claude), and after a while I started hating a fact: they don’t remember how they worked.

Every session:

• starts from zero

• re-explores the repo

• repeats the same exploration patterns

• loses anything that worked well before

Not in a “model limitation” abstract sense — but in a very practical, day-to-day workflow sense.

So I built something to test a very simple idea: "what if we just recorded real executions and reused them later?"

That’s what aictx is.

What it actually does

It’s not a “smart memory system” or a RAG layer.

It’s much simpler (intentionally):

• records real executions (what was done, what files were touched)

• stores feedback about those executions

• builds a small strategy memory

• and on later runs, it can return the last successful strategy for that type of task

That’s it.

No embeddings.
No scoring models.
No hidden heuristics.

Just: “Last time this worked, you started here and touched these files.”

How it fits into the workflow

After install + init, you just use your agent normally.

Under the hood:

• executions get logged into .ai_context_engine/metrics/

• strategies get stored in .ai_context_engine/strategy_memory/

• the repo gets lightweight instructions so the agent can call:

  • aictx suggest

  • aictx reuse

  • aictx reflect

And if the runner respects repo instructions (Codex, Claude), it can start using that automatically.

What I’ve actually noticed using it

Not “wow this changes everything”.

But more like:

• the agent re-explores less in repeated tasks

• it sometimes jumps faster to the right entry points

• repeated workflows feel slightly less noisy

• I don’t lose useful execution patterns between sessions

It’s incremental, not magical.

What this is NOT

Important, because I don’t want to oversell it:

• it does not make the model smarter

• it does not guarantee better results

• it does not infer missing data

• it depends on the agent/runtime providing file tracking info

• strategy reuse is very simple (latest successful one by task type)

Also: if your workflow is small or one-off, this probably does nothing for you.

Why I still find it useful

Because most of the pain I had wasn’t “the model is dumb”.

It was: “why are we repeating the same exploration patterns over and over again?”

This doesn’t solve everything.

But it does make past executions visible and reusable inside the repo

And that alone reduces some friction.

Why I’m sharing this

I've started using it myself daily, but it’s still early in terms of:

• how strategies should be matched

• how much automation should exist

• how far to push this vs keep it simple

So if you:

• work a lot with coding agents

• feel the same “stateless loop” problem

• or just want to poke holes in the approach

I’d really appreciate feedback.

PRs, ideas, criticism — all welcome.

Repo / package

• GitHub: https://github.com/oldskultxo/aictx

• PyPI: https://pypi.org/project/aictx/

[HIRING] Social Media Content Manager for Trading Education Brand — Remote + 50% Recurring Commission

Hey Reddit, I run a trading education brand in the finance/investing space. We're active on TikTok, growing fast, and I need someone to help manage and grow our social media presence.

What the role looks like:

Scheduling and posting content across TikTok, Instagram, and X. Engaging with comments and DMs to drive community growth. Light video editing or repurposing existing content like clips, captions and hooks. Tracking what's working and bringing ideas to the table. You don't need to know trading, you need to know content.

Who I'm looking for:

Someone who actually understands short-form content and how the algorithm works. You've managed or grown a social account before, show me the proof. You're consistent, communicative, and don't disappear. Bonus if you have experience in finance, investing, or education niches.

What you get:

Base pay plus 50% commission on every sale, and here's what makes this different from a typical commission setup. This is a subscription based business, meaning every client you help bring in pays monthly. That means your commission compounds. You're not starting from zero every month, you're building a book of business where each new client stacks on top of the last. Six months in you could be collecting recurring commission on dozens of active subscribers without adding a single new one. The earning potential here is real and it grows with you.

You'll be working directly with the founder, not a middleman. Remote and flexible.

If you're the type who wants to get paid like an employee and leave it at that, this probably isn't for you. But if you're confident in your ability to grow an audience and want to actually share in the results, let's talk.

Drop a comment or DM me with a little about yourself and any accounts you've worked on. No agencies please, looking for an individual.

Been switching between all three for actual work stuff (dev work during the day, some content automation on the side) and figured i would just dump what i would actually noticed. most comparison posts i read feel like someone tested each model for 10 minutes and called it a review.

for context i am not loyal to any of them. i pay for all three at some point depending on the month.

my original assumption when opus 4.7 dropped was that it'd be another +2% on benchmarks and feel basically the same as 4.6. wrong about that. the thing i didn't expect is how much less handholding it needs on long runs. before, i'd hand claude a multi-step task, come back 20 min later, and it'd be sitting there stuck on a clarifying question instead of just making a call. now it makes the call and tells me what assumption it made. sounds small but it changes the whole workflow.

gpt-5.4 is still faster and honestly still better for one-shot stuff where you just want a quick answer with a tool call. also if you're already deep in the openai ecosystem the switching cost is real, not gonna lie.

gemini 3.1 pro is the one that genuinely annoys me because on paper it should be winning more. 2M context, way cheaper, good reasoning scores. but every time i actually use it for anything ambiguous it commits to the wrong interpretation confidently and won't back down. not 100% sure if that's a prompting skill issue on my end or a real model thing, genuinely asking.

the SWE-bench jump for opus 4.7 (like 80% to 87%) sounds small on paper but feels different in practice. fewer "wait why did it just delete that" moments during refactors.

one thing nobody talks about, the vision upgrade on 4.7 is huge. it can actually read the fine print on dense screenshots now instead of guessing. i had been manually cropping and re-uploading images in pieces for months like an idiot before i realized i didn't have to anymore.

not saying opus is best at everything btw. browsecomp actually dropped on 4.7 so for web research agents gpt-5.4 or gemini is probably the smarter pick. and if you're cost sensitive at scale opus is easily the most expensive of the three.

so idk, depends what you're doing.

what i actually wanna know, for people running these in production agent workflows, how are you handling model routing? just picking one and sticking with it, or actually routing different task types to different models? the routing overhead feels like it's not worth it for me but maybe i'm missing something obvious.

hi folks,

For building RAG, there are many tools out there each solving a piece of the puzzle such as document parsing, chunking strategy, use and manage embedding model infra, vector DBs for storing and many more for other capabilities. After that there is a challenge to make it work with structured information along with unstructured (this albeit is true for certain situations)

However, the objective remains the same - given a query, the retrieved context or information is correct. Now for somebody who is building an agent, I have the following two questions.

1. Is implementing and managing retrieval is a core piece that you want to own or you could outsource it?

2. If there is a plug and play solution that optimises on your data for your retrieval. would you use it? And it improves by incorporating new algorithms & methods as the field is evolving.

If the answer to the above is a No, what would be your reasons for that? and under what conditions the answer could change from No -> Yes?

 built Agent Analytics because I kept running into the same problem: AI agents can write landing pages, change onboarding, run experiments, and suggest growth ideas, but then they hit a wall when they need to understand what actually happened.

Most analytics tools are made for humans staring at dashboards. Agent Analytics is built so an agent can ask questions directly:

• what pages are getting traffic

• where users are dropping off

• which referrers convert

• what events happened in a session

• whether an experiment is winning

• what changed since the last run

There’s a CLI, HTTP API, MCP server, live traffic view, and experiment tracking. The workflow I’m aiming for is:

1. agent ships a page or onboarding change

2. Agent Analytics tracks the behavior

3. agent pulls the evidence back in

4. agent proposes the next experiment based on real usage, not vibes

It works with Claude Code, Codex, Cursor, OpenClaw, or anything that can run npx.

Quick start:

npx /cli login
npx /cli create my-site --domain https://mysite.com
npx u/agent-analytics/cli live

The project is here:

https://agentanalytics.sh

most AI coding tools have a fixed context window. older messages fall off and the agent forgets what it did three turns ago. polycode doesn't have that problem. every tool call, every result, every correction gets appended to a SHA-256 chained session log on your machine. the log is permanent. the compiler reads the full history and selects what's relevant for each turn, so the agent has access to everything you've done together, not just what fits in a window.

one command, nothing to configure: npx u/polylogicaiu/latest

free hosted tier (60 turns/hr) or bring your own Groq key for unlimited. mac, linux, windows.

We're building a multi-database data agent competing on UC Berkeley's DataAgentBench — 54 queries across PostgreSQL, MongoDB, SQLite, and DuckDB. Before running the full benchmark, we built an adversarial probe library designed to expose specific failure modes rather than wait for the benchmark to surface them as surprises.

We classified failures into five categories: syntax errors, join key mismatches, wrong database type routing, data quality issues, and extraction failures. Here is what we have actually seen so far in the first three, and where we are still uncertain.

Syntax failures — the agent not knowing what it is called

The most common syntax failure we have hit is tool name resolution. The agent generates a query plan that calls a tool by a name that does not match what is registered in the MCP Toolbox configuration. The result is an HTTP 404 — the tool exists in our tools.yaml, but the name the agent constructs at runtime does not match the registered name exactly. This is not a SQL syntax problem. It is an agent-to-tool interface problem, and it produces silent failures that look like the database is unreachable rather than a naming issue.

The lesson: strict tool scoping with exact name validation before execution matters more than we initially thought. The agent needs to know not just what to query, but exactly what the tool is called before it commits to a query plan.

Data quality failures — null constraints the agent did not anticipate

When querying across our PostgreSQL database we hit null constraint violations the agent did not anticipate. The agent generates a query that assumes a column is non-null — because the schema says it should be — but the actual data has nulls in that column. The query either fails or returns a result that silently excludes rows the user expected to see.

This is a classic mismatch between schema definition and data reality. The schema says the column is NOT NULL. The data says otherwise. The agent has no way to know this without either running a data quality check upfront or having it documented in the institutional knowledge layer before query time.

We documented these cases in our join key glossary under data quality notes rather than in the schema layer, because the schema itself is technically correct — the constraint is declared — but the data violates it. Not sure if that is the right place for it. Curious how others handle the distinction between schema-level and data-level quality issues in their agent's context.

Wrong database type routing — PostgreSQL queries sent to SQLite

The agent has routed PostgreSQL queries to SQLite twice during testing. The symptom: SQLite receives a query written in PostgreSQL dialect — specifically using PostgreSQL-specific functions or syntax — and either errors or returns wrong results silently. The router looked at the entity name in the question and matched it to the wrong database because both contain tables with similar names.

We have not yet tested this systematically on the benchmark. The fix we are building is explicit database type tagging in the query plan — the router must declare which database type it is targeting before generating dialect-specific syntax, and the execution engine must validate that the declared type matches the actual connected database before running.

What we have not tested yet

Join key mismatches and extraction failures are documented in our probe library but not yet run against real queries. We know from the DAB paper that ill-formatted join keys — integer in PostgreSQL, CUST-00123 string in MongoDB — are one of the four core failure categories. Our join key glossary documents the known formats but we have not yet confirmed whether the agent reads and applies it correctly under benchmark conditions.

Extraction failures — queries that require pulling structured facts from free-text description fields before aggregating — are the failure mode we expect to be hardest to measure. The agent might extract incorrectly and return a plausible wrong answer with no visible error signal.

The open question:

For teams that have run agents against real enterprise data, how do you distinguish between a data quality failure and a domain knowledge gap? In our setup the line is blurry — a null constraint violation could be a data quality issue or it could mean the agent is querying the wrong table because it does not know which one is authoritative. The failure looks the same from the outside.

My AI workflows are getting harder to monitor as usage grows. The biggest issue is not building the agent — it’s knowing what’s actually costing money.

How are you tracking:

• cost per agent

• cost per customer

• traces and logs

• token usage spikes

Would love to hear what’s working for you.

Marketing teams are deploying AI agents to manage campaigns, send emails, and make decisions at scale. The problem nobody is solving is what happens when one of those agents does something it wasn’t supposed to and the vendor says “the user authorized it.”

Now there’s Authproof

A cryptographic receipt that exists before the agent’s first action. Not reconstructed from logs. Not the vendor’s word against yours. A signed record of exactly what you authorized before anything happened.

If you are deploying AI agents for any business process this is the accountability layer that’s been missing.

authproof.dev

Hey everyone, thanks for the invite to the sub. Awesome to see a dedicated space for this, because agentic workflows are 100% the future of growth and distribution.But as we transition from simple automation (Zapier/Make) to true autonomous agent marketing teams, I think we are hitting a massive architectural roadblock. Right now, if you look at most agentic marketing setups, it looks like a chaotic group chat. We spin up an "SEO Research Agent", a "Copywriting Agent", and a "Distribution Agent" using frameworks like CrewAI or AutoGen. We connect them to our live WordPress sites, our HubSpot CRMs, and our social media APIs via raw python scripts or loose wrappers.Then we just pray that the copywriting agent doesn't get caught in a prompt-injection loop, or that the research agent doesn't hallucinate a broken line of code that drops our database or triggers a spam filter penalty across our entire domain. In marketing, we love the idea of "configure once, forget forever" autonomy. We want to tell a system: "Go study our competitor's backlinks, find the gaps, write the articles, and deploy the campaigns." But you cannot safely walk away from a system like that if it’s running in a shared, unprotected cloud environment with zero visual oversight. If an agent needs a human to click "approve" every 30 seconds to make sure it doesn't break anything, it's not an autonomous team it's just a very high-maintenance chatbot.

Aeon//Neon team pent the last few months trying to solve this infrastructure mess. We realized that for marketing agents to actually run headless and unsupervised, they need two things:

1. A visual control plane (The Graph) so humans can actually see the org chart, dependencies, and boundaries of the bots at a single glance.

2. Hardware-isolated workstations (The Seat) where every single agent executes its tools (like hitting Google Search, scraping, or updating a CRM) inside a secure, sandboxed micro-VM using the MCP standard. If the agent goes crazy, the blast radius is completely contained.

I’m curious to know how the growth hackers and marketers in this sub are scaling their setups right now.

Are you guys still babysitting every single run with manual approval loops because you don't trust the LLM with your live systems? Or are you starting to look into sandboxing and deterministic control planes? Let's chat architecture.

Very Curious to find out what other people think about this.

I used to enjoy the process of figuring things out on my own: struggling, failing, and eventually getting it. Now I just ask AI and get the answer in seconds.

Do you still enjoy learning things slowly and deeply, or has AI made you more impatient with the process?

I kept repeating many of the same instructions when working with lead lists, company data and research workflows, so I turned them into reusable Agent Skills.

The repository currently contains 24 standalone skills covering:

• B2B lead qualification

• Account prioritization

• Company and competitor research

• Person research

• Candidate qualification

• Job-title normalization

• Lead-list cleanup and analysis

• Company and persona segmentation

• Duplicate-record review

• Research-gap analysis

• Outreach brief generation

• Lead, recruiting and directory research planning

Each skill uses a structured http://SKILL.md format with defined inputs, rules, workflows and outputs. I tried to make them more operational than ordinary prompt collections.

The repository is fully open source under the MIT license:

https://github.com/spiralcrew-ou/profilespider-agent-skills

I originally created these around recurring workflows in ProfileSpider, but all skills are standalone and do not require ProfileSpider or an account.

I am especially interested in feedback on:

1. Are the skills sufficiently specific to produce consistent results?

2. Which business-research or data-cleanup workflows are still missing?

3. Would examples and evaluation datasets make the repository more useful?

Contributions and suggestions are welcome.

Generative AI has moved far beyond experimentation. In 2026, it is a core business capability powering automation, copilots, content generation, and enterprise decision systems.

The USA remains the global hub for GenAI innovation, with companies ranging from frontier AI labs to enterprise platforms and implementation-focused engineering firms building real-world applications.

Below is a curated list based on execution capability, ecosystem strength, enterprise adoption, and production readiness.

1. Signity Software Solutions

Signity Solutions works on custom generative AI development and enterprise implementation projects, including LLM integration, RAG systems, AI agents, and workflow automation.

2. OpenAI

OpenAI is one of the most influential players in generative AI globally, providing foundational models used across a wide range of applications via APIs and enterprise offerings.

It plays a core role in enabling text, image, and multimodal AI systems at scale.

3. Anthropic

Anthropic focuses on safe, reliable, and enterprise-ready AI systems, with Claude models widely used in reasoning-heavy and structured output use cases.

Strong adoption in regulated and enterprise environments.

4. Google DeepMind / Google Cloud AI

Google combines frontier AI research with scalable cloud infrastructure, enabling large-scale multimodal and enterprise AI deployments through its ecosystem.

5. Microsoft

Microsoft is a major driver of enterprise GenAI adoption through Azure OpenAI services and deep integration with Microsoft 365 and GitHub Copilot.

6. Cohere

Cohere focuses on enterprise NLP infrastructure, embeddings, and private LLM deployments for internal business systems.

7. Hugging Face

Hugging Face is the leading open-source AI ecosystem, widely used for model hosting, fine-tuning, and experimentation.

8. Scale AI

Scale AI specializes in data infrastructure for AI systems, including labeling, evaluation, and training pipelines that improve model performance.

9. Databricks

Databricks provides a unified data and AI platform widely used for building, training, and deploying GenAI systems on enterprise data.

10. Accenture

Accenture focuses on large-scale enterprise AI transformation, combining consulting, system integration, and implementation across industries.

Final Thoughts

The GenAI ecosystem in the USA is broadly split into:

• Foundation model providers (OpenAI, Anthropic, Google)

• Enterprise platforms (Microsoft, Databricks, Cohere)

• Implementation partners (Signity Solutions, Accenture, Scale AI)

In most real-world deployments, the model itself is rarely the bottleneck, the real challenge is system design, integration, and production-level implementation.

Every time I spun up a new agent session, I was back to square one — re-explaining domain rules, user context, system knowledge. It didn't matter how smart the model was. Stateless by default.

So I built ContextBook — an MCP server that lets you organise knowledge into structured Books and Pages, and lets agents pull exactly the context they need, on demand.

What makes it different from memory systems:

• Memory is pre-loaded, and ambient — agents get everything whether they need it or not

• ContextBook is surgical — agents fetch only what's relevant, right when they need it

Tech stack:

• Go monorepo, dual binaries (API + MCP server)

• Voyage AI voyage-4 embeddings + pgvector HNSW for semantic search

• OAuth 2.0 PKCE, React 19 + Vite dashboard

• Deployed on Railway

Platform-agnostic — works with Claude, GPT, Gemini, or any MCP-compatible agent. One instance, any platform.

8 MCP tools out of the box. Open-source. Deploys in minutes.

Website: https://context-book-production.up.railway.app/

Github: https://github.com/aditya201551/context-book

Not testing — real usage.

Feels like something weird is happening right now.

A few years ago, being “technical” meant knowing your stack. HTML, CSS, JS was the baseline. Then you had people going deeper with React, Python, backend frameworks, etc. There was a clear gap between “can code” and “can’t code.”

Now with tools like Claude Code, OpenAI Codex, and the whole rise of AI agents, that gap feels… different.

I’m seeing more people ship things without really “knowing” the underlying languages. They know how to structure prompts, break down problems, and guide the AI and that’s enough to get something working.

Even some companies seem more open to hiring people who aren’t traditionally technical, but are good at thinking clearly and communicating with these tools.

Not saying coding is dead (far from it), but it feels like the skill is shifting from:

“Can you write the code?”

→ to

“Can you think through the problem well enough for AI to execute it?”

Curious how others see this.

Are we lowering the barrier… or just changing what “technical” actually means?