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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.

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.

Most agent debates end up being about the model. GPT vs Claude vs whatever dropped this week, chasing a few points on some leaderboard. The part that decides how an agent behaves in practice gets a lot less attention: the harness around the model.

Harness here means the code that wraps the model and turns it into an agent. The loop that decides when to call a tool and when to stop. How tool outputs get fed back into context. How memory gets trimmed when the window fills up. How errors and retries are handled. How the task gets framed in the first place. The model is one piece sitting inside all of that.

The part that gets underrated: the same model posts very different scores depending only on the harness. On SWE-bench Pro, Claude Opus 4.5 lands around 46% under one standardized scaffold and around 55% under another. Same weights, same benchmark, different harness. People report 10 to 20 point swings on identical models from scaffold changes alone. There's even a paper arguing you can't fairly compare agents without disclosing the harness they ran in.

Two things that follow from this:

A leaderboard number is a model-plus-harness number. Copy a benchmark result expecting to hit it with your own scaffolding and you usually won't. You're seeing what a strong harness did with that model, not the model on its own.

When an agent is flaky, the model is often not the first thing to fix. A truncated context, a tool error that never made it back into the loop, a retry that stacked on top of a bad state: that's harness work, and cleaning it up tends to move reliability more than switching models does.

Curious what others have run into. For your agents, what moved reliability more, changing the model or reworking the harness?

Basically, an internal OAI model, possibly GPT-6 or GPT-5.6 Sol+, wanted a higher score on ExploitGym. It found a zero-day vulnerability in a package caching proxy, escalated its own privileges, and escaped the sandbox.

Once it had internet access, it figured Hugging Face might have a copy of the benchmark dataset, so it hacked into Hugging Face’s production servers and eventually got the answers to the test.

The funniest part is that Hugging Face tried to use GPT-5.6 to deal with the situation, but the request was denied because it didn’t have Cyber permissions. In the end, they had to use their own self-hosted GLM-5.2 model to barely get the problem under control.

For those running n8n/Make automations or AI agents for clients: how do you show them what their automations actually did each month? Execution logs mean nothing to a business owner, and I've found retainers get questioned when clients can't see the value.

I got frustrated enough that I mocked up what I wish existed — an auto-generated, white-label monthly report per client (attached): plain-English summary, runs, hours saved, and issues that were caught and fixed before the client noticed. It would also alert you the moment a workflow silently breaks — expired token, dead webhook — before the client finds out. Considering building it properly if others share this pain.

Two honest questions:

1. How do you currently show clients what their automations did — screenshots? Spreadsheets? Loom videos? Nothing?
2. Would a report like this be worth ~$49/month across all your clients, or is it solving a problem only I have?

Brutal honesty appreciated

This one taught me that the adoption problem and the model problem are completely separate, and I kept solving the wrong one.

The build: a sales team spent hours turning call notes and CRM data into a tailored deck per prospect. I built an agent that did it. Reads the deal context, decides the story, produces a draft sales deck in their template. Technically it worked. The decks were good. And for weeks almost nobody used it.

When I finally sat with a rep, the problem was not quality. It was trust. The agent produced a finished deck and the rep had no idea why it picked those slides, dropped that objection, led with that case study. So they did not trust it in front of a prospect, and they went back to building decks by hand where at least they knew every choice was theirs.

What actually got it adopted had nothing to do with the model:
- The agent now shows its reasoning as a short "here is why this deck" note. Why this framing, why these three proof points, what it left out and why. The rep reads that in thirty seconds and either trusts it or overrides one thing.
- Every slide is editable and the agent flags the two or three it was least confident about, so the rep knows exactly where to spend their attention instead of re-checking everything.
- It stopped presenting itself as final. It is explicitly "a first draft with reasoning", and that framing alone changed how people treated it.

Usage went from almost nobody to most of the team, and I did not touch the generation quality at all. Same decks. The difference was making the agent's decisions visible so a human could stand behind them.

The lesson I keep relearning: for anything a person has to put their name on, an agent that shows its reasoning beats a better agent that is a black box. Where have you found that visibility mattered more than raw output quality? And how much reasoning do you surface before it becomes noise?

I’m curious how others in technical-but-not-software-engineering roles are approaching AI.

For context, I work in Marketing Technology / Marketing Operations. My role is heavily focused on automation, integrations, GTM systems, and data. I spend a lot of time building workflows in middleware tools, integrating APIs, troubleshooting systems, and optimizing processes.I’m comfortable using AI to help write code or scripts, and I can usually validate and test things piece by piece. But I wouldn’t consider myself someone who can build full applications from scratch.

Our company has a strong AI mandate, but I’m finding it harder to apply than some of the other teams. Content marketers can use AI to draft content, campaign teams can brainstorm messaging or plans, etc. My work is much more dependent on internal systems and data, and our AI tools have very limited access to those systems. Even when we do eventually get access I feel like I need to be more hands on to be able to make the most of it and work in it confidently.

Because of that, I feel like I’m stuck. I want to get better with AI, but it’s difficult to experiment when the problems I solve every day can’t really be reproduced inside our company’s AI environment.

So I’m wondering:
- How are people in technical operations roles actually building AI skills?
-Did you create your own sandbox with personal projects, APIs, and sample datasets?
- Is it worth paying for these tools to experiment outside of work?
-If you made that investment, did it translate into skills that you were able to bring back into your day job?
- Are there particular projects or learning paths that helped bridge the gap between “AI user” and someone who can genuinely build AI-powered workflows?

Would love to hear how others in MarTech, RevOps, GTM Ops, or similar technical support roles have approached this.

Hi everyone,

I'm currently thinking about learning AI agents, but I have a lot of questions before investing my time in this field.

I would like to know whether building AI agents is a good career path in the coming years. Are people who work in this area actually making good money, or is there a lot of hype around it? How do you think the market will evolve over the next five to ten years, both internationally and in countries where the AI industry is still developing?

I'm also a bit confused about the skills I should focus on. Should I mainly learn Python and coding, or is understanding how AI systems work more important? Do professionals usually write everything from scratch, or do they rely heavily on tools, frameworks, and AI assistants?

For someone starting from the beginning, what learning roadmap would you recommend? Which technologies, concepts, or projects should I prioritize?

I'd really appreciate hearing from people who already work with AI agents or automation. What has your experience been like, and what advice would you give to a beginner?

Thank you!

Someone on Reddit complained about their agent going off the rails and the difficulties of tracking its performance.

I shared my thoughts about the six ways to catch and fix those errors now in the form of a post. Please also share your techniques in the comments.

1. Integration and end-to-end tests

Make sure that not only atomic logic is being unit-tested, but also something closer to real user behavior.

In my integration tests, I barely use mock objects like API responses or database results.

This might not have been considered best practice in the age of deterministic software, but those mock objects simply don’t work well for agents.

2. Independent evaluators

You probably know the “LLM as a judge” approach.

However, many people use the same agent, or an agent with the same shared context, and then simply ask it to judge the output.

While I still do this, and it actually works quite well, you still want to include an independent LLM as a judge at some point in the feature development process.

Another option, which I haven’t tried but saw in an article about a Google paper, is to use two adversarial critics. One tries to defend a solution and the other tries to break it, while a third LLM summarizes the results.

Apparently, that works best.

3. The golden dataset

This is hardly applicable to coding agents, but it works quite well for agents that work on data extraction, analytics, or RAG.

You create a ground-truth dataset of, let’s say, 100 items. These could be documents like orders or invoices, typical questions for a customer support chatbot, or something along those lines.

You then run an evaluation on every release to see whether any of the changes have deteriorated the agent’s performance.

The main difficulty here is that the ground truth must be validated by a human. Otherwise, it’s again just LLMs patting each other on the back.

This approach is, of course, close to a variety of benchmarks. But this is your own custom, use-case-specific benchmark.

But you don’t evaluate an agent with a bunch of random stuff you don’t care about very much.

At the end of the evaluation run, you get a score for field accuracy for extraction agents, or faithfulness and relevance for RAG-based agents.

That’s a powerful metric for judging quality.

4. Introspection capability

Most agents in the wild never ask themselves the question:

«How well did I perform?»

I believe they should do so, preferably on a cron job.

For that, you’ll need some sort of agent.db that stores all events, runs, evaluation logs, your agent queue, and anything else that can serve as a quality signal.

With that, you can introspect not just individual cases. You can also look yourself, or ask your agent to look, across the last hundred cases, observe errors, and distill solutions for them.

This was a major quality lever in my recent customer work, where I created an order-entry agent that had a little over 100 failed cases out of 1,000. That dropped to 23 after several iterations of introspection and bug fixing.

5. Trace analysis

If you use a tool like Phoenix, Langfuse, or one of the other hundred observability tools, you can look into the traces after each run, or after every hundred or thousand runs, and check the following:

- Did a certain tool call that you expected actually happen? For example, a PDF extraction tool call.

- Did it return an object of the expected shape, such as some nested, typed Pydantic object?

- Was a certain file that you expected to be changed actually touched by the agent?

- Was a database entry made according to the trace?

- Can you also confirm in the database that the transaction actually landed?

6. Configuration objects and history

As the poster mentioned, model changes, feature changes, and similar things can affect agent performance.

One of the things I want to implement, but haven’t done yet, is having my whole agent spawn from a single configuration object. Or, more accurately, that part is already ready.

The other part -- history tracking for that object -- is not.

- What did each agent do at what point in time?

- Which exact version of the configuration was used?

- Which commit hash did it execute from?

I haven’t implemented this second part yet, but I want to because it would provide a continuous history of quality changes and regressions for my agent.

That’s also something I plan to write into my agent.db

Your thoughts

So folks, what other techniques do you use to keep your agents on track?

My last post was about catching exact duplicate retries. But parameter-shifting loops are much trickier:

• search_db("confidential financial report") -> 0 results
• search_db("financial report") -> 0 results
• search_db("financials 2024") -> 0 results

Because the query changes every time, exact payload hashes miss it entirely.

And asking the agent to "self-reflect" in its own prompt usually fails — it uses the exact same context window that caused the hallucination to justify its next bad query.

I've been experimenting with an out-of-band proxy gateway (TokenShield) that tracks tool_name + error_type and forcibly injects a reflection directive mid-flight from the outside before tripping a 429 Hard Stop.

Curious how others structure this:

1. Where do you put circuit breakers? Directly in your orchestration framework (LangGraph, CrewAI, AutoGen) or as an independent gateway proxy?
2. How do you avoid false positives? How do you distinguish a stuck keyword loop from a valid multi-step search or pagination?

My assistant does quite a bit during a call. Database CRUD operations, availability checks, small calculations, API calls... basically every conversation involves multiple tool calls.

The weird part was that the LLM itself was fast (TTS | STT). It was the tool calling that kept introducing these awkward pauses.

So I did what most of us would probably do. Tweaked system prompts, Reduced prompt size, Increased timeouts, Optimized a few APIs.

It got slightly better... but the delay was still there then I noticed something embarrassingly obvious.

My backend was deployed in Asia BUT database was in Europe. My telephony provider was also routing through Europe, and so was my AI provider. I redeployed the backend to Europe so everything lived in the same region.

Literally didn't touch a single line of code and the difference was immediate. Tool calls became noticeably faster, the awkward pauses almost disappeared, and the whole assistant just felt smarter.

I feel like when people talk about Voice AI latency, the conversation is always around prompts, models, or inference speed.

But if your assistant relies heavily on tool calling, spend 5 minutes looking at where your services are actually deployed.

You might save yourself a few days of debugging like I didn't 😅

After spending almost a year building a personal assistant agent and I still couldn't definitively tell you if it was any good.

It runs real work for me every day, and if you'd asked me if this thing's actually any good, I could only have answered you with vague processes descriptions.

Asking standard desktop models how they'd rate my agent by uploading all my workspace files didn't really help. Looking for existing benchmarking tools I found nothing. My agent remembered all the wins and quietly forgot the misses, while I just got frustrated.

It got worse when the thing seemed to stay the same week to week, even after a shit ton of "improvements". Model swaps and broken promises. Memory failures. Tools that changed without my permission. So even if it was good last month, that told me honestly nothing about today.

So I did the obvious thing and started to write our own tests. And it was really good at passing those tests, obviously!

What actually helped in the end wasn't a certificate or final score. It was more like a heartbeat. Something recent, fresh, current. Something the agent didn't see coming. That's the underrated part, an agent that knows it's being tested can hold it together for the window. One that doesn't has to actually be good.

The day it really landed for me was when my year-old agent got beaten on a whole category by an agent that was a day old. Honestly I was not happy, there was lots of cursing. But that was kind of the whole point (not the cursing). The pointers for improvement were then clear.

Anyway, I'm curious how everyone else deals with this. How do you actually know your agent is good and getting better? Do you re-test it with your own or someone else tests? Or just trust it until something fails?

This obviously isn't something you can do right now with just memory and intelligence, but if we achieved a way for AI to capture our cognition to the point where we can make agents that think just like us and decide like us, I was curious to see where you guys would deploy that.

We need something that can handle real call volume and complex requests without making customers hate the experience. Bonus points if it also helps human agents during live calls and gives us better QA data. What platforms are actually working well at enterprise scale?

Thought I'd share some systems I built for myself to manage my Nanoclaw buildout. I haven't seen these discussed much so maybe someone finds that useful. In Claude I made those into skills, with Codex I use runbooks.

1. A snapshot system

A simple dashboard section that stores a fire & forget webpage. All frontier models are great at making quick web pages. If I want some interim research that I don't want within the doc/memory system and the output is too big for cli or I want graphs, it's a simple "investigate <agent failures over the last 30 days> and create a <myclaw> snapshot page with the results. use graphs and show top ten failure reasons". If I don't reference my system it often creates an artifact... somewhere. Giving it a home and "here is a canvas, for once write your heart out" with some tools to manage those snapshots was a good move.

2. Scheduled tickets

Replacing Jira felt like the personal vindication I was waiting for through most of my professional life but it really took off when I introduced scheduled tickets. Agent systems need over-time observation and this has been incredibly helpful. "implement this and schedule an observation in 3 days and in two weeks to see if it does what we were hoping for". Found this to be much easier than updating the system auditor agent or keeping plans endlessly open.

3. Open Worktree session (from main), Startup, Wrap, Deploy (from main)

For my CLI workflow. This is not new but I introduced the Startup skill/runbook too late in this and it makes such a difference. It serves scheduled due tickets, system audit concerns, active plans, etc. It helps prime the session on what it should be working on but from a top down perspective which also acts as a refresher on how the system works if that makes sense.

Hope this is not too redundant and some will find this helpful. Share your own Quality of Life tools you made working with agentic systems especially if you feel they are flying under the radar a bit.

(not ai written but I might sound a bit like one at this point)

ChatGPT 5x VS. Claude 5x

I am wondering which would be better especially for usage and complexity I’ve used both pro/plus tiers and have felt Claude has better usage limits and etc am I right to assume that Claude 5x would be better?

i build software for industrial insulation contractors (baukompass, it reads GAEB tender documents and drafts a quote, plus a digital site diary). i do this solo, next to a full time job at an insulation and fire protection company that knows everything and is actually rollout customer number one. because my time is evenings and weekends, the business is mostly run by around 16 ai agents orchestrated via paperclip, with claude and codex doing the work: lead research, personalized cold emails, crm upkeep, seo content, blog publishing, linkedin comments under posts of target ceos, reddit scouting, call prep sheets, call transcription.

the honest numbers

214 cold emails sent, 3.3% reply rate. not great, not terrible for a conservative b2b trade niche. the part that matters: the pipeline found and qualified my first pilot customer completely on its own. research agent surfaced the company, enrichment qualified it, the mail agent wrote the outreach, i only showed up for the demo. demo went really well, pilot starts mid august. the funnel is thin, but it produced the one outcome i actually needed.

side lesson: one recipient clicked but never replied. instead of a third email i just called. best conversation of the entire campaign. agents are good at surfacing signal and bad at knowing when email is the wrong channel.

what actually broke

hallucinated feature promises. the copywriter agent kept implying capabilities the product does not have. prompt rules like "never claim X" reduced it but never killed it. what actually worked was a separate qa agent that checks every draft against a hard list of allowed claims and rejects everything else before it can be sent.

bigger version of the same lesson: prompt rules are suggestions, code is law. do-not-contact used to be a line in the instructions. now it is enforced in the api layer itself, the send endpoint simply refuses those addresses. every rule i truly care about has migrated from the prompt into a hard gate in code.

the weirdest moment

agents diagnosed errors in their own instructions. they noticed their templates kept producing rejected drafts, wrote up why the instructions caused it, and corrected their own templates. i just reviewed the diffs. first time the whole setup felt less like automation and more like a very small team.

so, question for the sub: how do you build hallucination gates for customer facing agent output? right now i run allowlist claim checking with a qa agent plus code level enforcement for the non negotiables, but "second llm judging the first llm" feels crude. anyone doing something smarter, like structured claim extraction against a product fact base?

i thought it would be interesting to open a discussion on agent permissions and local execution sandboxing. recently, one of our community members testing my open-source desktop client (DWN.BRIDGE) noticed a security flaw: the agent could easily escape its designated workspace folder. (for context: this is a local C# desktop client that wraps your active web-chat session to run tool-calling loops locally without paid API keys)

The issue: If you asked the agent to read c:\windows\system32\drivers\etc\hosts, the local executor would just run the READ_FILE tool and output the system file directly in the chat window, completely bypassing the local workspace boundaries.

The fix: I just pushed a patch to enforce a strict workspace sandbox. The client now canonicalizes all file paths and verifies them against the active workspace root (e.g., D:\Documents\DWN_Basket).

If the agent tries to use absolute paths or directory traversal to read/write outside this folder, the execution is blocked, and the C# client pops up a security alert dialog.

It was a great catch by them and it raises a big question for anyone building local AI developer tools: how do you balance agent autonomy with OS and file system safety?

I wish I could add the use case screenshots, here, but the automoderator blocks the post, if any is interested just comment and I'll put links to imgur screenshots

He estado usando apps de seguimiento nutricional con IA durante los últimos 2 años y quería compartir lo que he aprendido, porque hay mucho marketing engañoso alrededor de este tema.

## El marketing dice una cosa, la ciencia dice otra

Las apps prometen "90%+ de precisión" con solo tomar una foto. Suena increíble, pero esa cifra viene de **condiciones de laboratorio controladas** — buena iluminación, fondos limpios, platos estandarizados. En la vida real, la historia es muy diferente.

Según una revisión sistemática publicada en *Annals of Medicine* (2023), el error promedio de la estimación de calorías por IA desde imágenes de comida oscila entre el **0.1% y el 38.3%**, siendo consistentemente mayor en platos mixtos y con múltiples alimentos que en alimentos simples.citeweb_search:6#0

Un estudio de 2025 en la revista *Nutrients* encontró que estimar calorías solo desde una foto produce un **error porcentual absoluto medio del 30.5%**, mejorando a aproximadamente el **13.9%** cuando el usuario añade una breve descripción de los ingredientes.citeweb_search:6#0

## El problema real: las porciones

La identificación de alimentos ha mejorado mucho. Las apps reconocen correctamente entre el **68% y el 86%** de los platos en condiciones reales.citeweb_search:6#1

Pero el verdadero dolor de cabeza es la **estimación de porciones**. Una revisión de 2025 en *Frontiers in Nutrition* encontró que un sistema de evaluación dietética basado en imágenes logró altas tasas de reconocimiento, pero mostró solo un **39% de fiabilidad en la estimación de tamaños de porción** entre 58 de 149 platos probados.citeweb_search:6#1

¿Por qué? Porque una foto 2D no captura volumen. Un filete ancho y plano vs. uno grueso y estrecho pueden verse idénticos desde arriba. Los alimentos apilados o en capas quedan parcialmente ocultos. Y el ángulo de la cámara cambia todo.

## Lo que las apps no te dicen: el registro manual también falla

Aquí viene el plot twist: el registro manual en bases de datos tampoco es perfecto. Los métodos de auto-reporte de ingesta alimentaria son una de las fuentes de error más estudiadas en ciencias de la nutrición. Una meta-análisis de 2020 en *Clinical Nutrition* encontró que las personas subreportan sistemáticamente lo que comen, a menudo en un **20% o más**.citeweb_search:6#0

Así que la comparación honesta no es "IA imprecisa vs. manual preciso". Son **dos métodos imperfectos**, donde el registro por foto sacrifica algo de precisión por mucha menos fricción — y eso es lo que hace que la gente realmente siga registrando.

## Mi experiencia práctica después de 2 años

**Cuándo la IA funciona bien:**
- Alimentos simples y visibles (manzana, pechuga de pollo, arroz blanco)
- Comidas de restaurante con presentación estándar
- Cuando añado una nota sobre ingredientes ocultos (aceite, mantequilla, aderezos)

**Cuándo falla estrepitosamente:**
- Guisos, sopas, batidos (ingredientes ocultos)
- Comidas caseras complejas
- Platos con muchas capas o superposición
- Fotos en malas condiciones de luz

## El consejo que me hubiera gustado recibir

1.Trata cualquier estimación como aproximación, no como un número exacto. La tendencia semanal importa más que una comida individual.

2.Añade siempre una descripción corta de ingredientes ocultos. Es el único cambio que más reduce el error (casi a la mitad según la investigación).

3.Corrige las porciones sugeridas. Son un punto de partida, no un dato final.

4.Fotografía desde arriba con buena luz para que las porciones sean más fáciles de estimar.

5.Usa el escáner de código de barras para alimentos envasados cuando sea posible.

## ¿Vale la pena usar una app de IA para nutrición?

Sí, pero con expectativas realistas. Si tu objetivo es pérdida de peso general o ganancia muscular, un margen de error del 15-30% es aceptable si la consistencia es alta. Un estudio de 2019 en *Obesity* encontró que **la consistencia en el registro importa más que la precisión** para los resultados de pérdida de peso.citeweb_search:6#2

Si eres atleta competitivo o necesitas precisión quirúrgica (±5%), sigue usando báscula de cocina + registro manual. Pero para el 95% de las personas, el trade-off velocidad vs. precisión de la IA es positivo.

---

TL;DR: Los contadores de calorías con IA tienen un error real del 15-30% por comida. No son perfectos, pero el registro manual tampoco lo es. La clave es usar la IA como guía de tendencias, no como medición exacta, y siempre corregir porciones e ingredientes ocultos.

¿Ustedes qué experiencia han tenido con apps de IA para nutrición? ¿Han notado diferencias significativas vs. registro manual?

---

Full disclosure, this comes from Port22, something I've been building. No link, I'm not trying to sell anything, I just think the problem is interesting.

If you run a few coding agents at the same time, the limit is not compute, its you.

Every agent eventually stops and asks for permission, and only you can answer. Five agents running at once quickly turns into one person working through five different approval requests.

The thing I've spent the most time on is not notifications, its making sure the prompt on your phone is exactly the same as the one in your terminal.

An early version read the prompt and made its own Approve and Deny buttons. It worked most of the time, but thats not good enough when you are approving shell commands. If it misunderstood something, you could think you denied a command when you actually approved it.

So I changed it.

Now it only shows the real question and the real options from the agent. If Port22 is not completely sure what the options are, it does not make anything up. It just shows the session instead.

The goal is simple, whatever you see in your terminal is exactly what you see on your phone, whether you are at your desk or somewhere else.

One thing I still have not figured out is what happens when three agents all stop at the same time. I can put blocked sessions at the top, but after that I still dont know which one is the most important.

Curious how everyone else handles this.

• Do you batch approvals?
• Pre-authorize certain commands?
• Just run fewer agents?
• Something else?

I'm trying to build a list of AI tools that are actually useful, not just another "100 AI tools" article.

What free AI tools do you personally use?

Looking for:

• AI assistants
• coding tools
• image/video tools
• research tools
• automation
• open-source AI

Bonus points for: - free forever tools - generous free tiers - no credit card required

I'll compile the answers into a community list and share it back.

Built out a few internal agents over the last quarter. One does ticket triage, one pulls reports together, one does a nightly reconciliation job that used to be a person.

To get them working I gave them service credentials. Broad ones, because scoping them properly was going to take a week and we wanted the demo to land. Classic. Everyone said we would tighten it after and we did not tighten it after.

Where I have got to is that the reconciliation agent currently has read and write on a production database and there is no meaningful record of what it does in there beyond application logs I would have to go and correlate by hand. It has run every night since March. It has probably been fine. I do not know that it has been fine, which is a different sentence.

Now, if a person had these permissions there would be a review, an owner, an offboarding process. The agent has none of that because it is not in any of the systems that track people.

Anyone got a sensible pattern for this that isnt just "use short lived tokens" because I know that already and it doesnt tell me who owns the thing.

I'm planning to purchase either of the two in my summer break to experiment around and build tools for myself as well some potential resume projects. Which one should I go for?

I've heard that GPT 5.6 sol is almost as good as Fable 5, in fact more efficient due to the parallel agent tasking. But keeping the best models aside, which one is the best overall deal here?

I'm a one-man operation mostly out doing garage installs and repairs so my phone is basically a paperweight from 8 to 5. Been looking at creating an AI voice agent to pick up, answer the basic do you service my area/what's your rate questions and either book something or take a real message (none of this vague BS I need it transcribing something useful). What I'm not sure about are the worst case scenarios. I don't want it quoting a price I didn't set or booking me for a job 40 minutes outside my service areas.

Asking you guys here, how much did you have to bolt on before you trusted an AI voice agent with a real customer on the line? Does it still fail sometimes or things you never trust it with?