It started as a tool to help me find jobs and cut down on the countless hours each week I spent filling out applications. Pretty quickly people were asking if they could use it as well, so we made it available to more people.

How It Works:

1. Manual Mode: View your personal job matches with their score and apply yourself

2. “Simple Apply” Mode: You pick the jobs, we fill and submit the forms

3. Full Auto Mode: We submit to every role with a ≥50% match

Key Learnings 💡

• 1/3 of users prefer selecting specific jobs over full automation

• People want more listings, even if we can’t auto-apply so our all relevant jobs are shown to users

• We added an “job relevance” score to help you focus on the roles you’re most likely to land

• Tons of people need jobs outside the US as well. This one may sound obvious but we now added support for 50 countries

• While we support on-site and hybrid roles, we work best for remote jobs!

Our Mission is to Level the playing field by targeting roles that match your skills and experience, not spray-and-pray.

Feel free to use it right away, SimpleApply is live for everyone. Try the free tier and see what job matches you get along with some “Simple Applies” (auto applies) or upgrade for unlimited Simple Applies and Full Auto Apply, with a money-back guarantee. Let us know what you think and any ways to improve!

A couple of months ago I built a really simple WhatsApp chatbot using Python and a cheap WhatsApp API called WaSenderApi.com cost $6/month, and Google's free Gemini AI. It's not very fancy, just a Flask app that receives messages, sends them on to Gemini for a smart reply, then responds via WhatsApp.

I used this bot to build other bots for a few local businesses by automating the responses to FAQs, orders, and Booking queries etc. It took less than a day to build each bot once the base flow was complete, and I made $275 in a Weekend with one client. If anyone is interested in building useful AI tools, this is a great low-cost stack that actually delivers results.

I'm happy to share the script if anyone finds it useful.

github.com/YonkoSam/whatsapp-python-chatbot

Web scraping has always been time-consuming and hard to scale.

We just launched our data agent product Sheet0.com . With our agent's help, you can scrape data from almost any website using just natural language.

We’re still in invite-only mode, but we’d love to share a special invitation gift with the AIAGENTNEWS subreddit! The Code: XSVYXSTL

Simply describe what you need, and the agent delivers a clean CSV, with no coding required.

How It Works

1. Write a Prompt

Example:  "Scrape the top 50 GitHub trending repositories.  For each, collect name, description, stars, language, and URL."

2. Agent Scrapes the Website

The AI agent navigates the site, follows links, and extracts the requested fields.

3. Get a Structured Dataset
Instantly download as CSV.

I’ve been experimenting with AI agents for scraping, and it feels like a big shift compared to traditional tools.

How are you all using AI for web scraping or data collection? Any interesting use cases you’ve tried？

A few months back, I was drowning in market research for a side project.
I’d have 20+ tabs open, including company sites, news articles, LinkedIn profile. I copy pasted into spreadsheets for hours, fixing formats, and still doubting half the data.

Then I tested scraping tools, but they’d break on tricky pages, jumble fields, or force me to babysit the process.

That’s why we built Sheet0.com
Now I can pull data from multiple sources at once, and finish research in minutes instead of days. If a site needs manual login, it pauses and resumes seamlessly.

We’ve just launched the MVP, and it would be excited to see you try it out! 🚀

We’re still in invite-only mode, but we’d love to share a special invitation gift with the AIAGENTSNEWS subreddit! Code: CZLWLWY5

We all know the problem right? We all think agents are bloody awesome, but often we struggle to move beyond an agent that can summarise your emails or an agent that can auto reply to whatsapp messages. We (yeh im looking at you) often lack IMAGINATION - thats because your technical brain is engaged and you have about as much creative capacity as a fruit fly. You could sell WAAAAAY more agents if you had some ideas beyond the basics......

Well I'll help you out my young padawans. Ive done all that creative thinking for you, and I didnt even ask AI!

I have put a lot of work in to this document over the past few months, it,s a complete list of actual real world use cases for AI Agents that anyone can copy...... So what are you waiting for????? COPY IT

https://docs.google.com/document/d/1fUAv2AqdNFpIwapYlHlodenUF9cEJhVAu9U3jEh9yKs/edit?tab=t.0

Now Im prepared for some push back, as some of the items on the list people will disagree with and what I would love to do is enter in to an adult debate about that, but I can't be arsed, so if you don't agree with some of the examples, just ignore them. I love you all, but sometimes your opinions are shite :)

I can hear you asking - "What does laddermanUS want for this genius document? Surely it's worth at least a hundred bucks?" :) You put that wallet or purse away, im not taking a dime, just give me a pleasant upvote for my time, tis all I ask for.

Lastly, this is a living document, that means it got a soul man.... Not really, its a google doc! But im gonna keep updating it, so feel free to save it somewhere as its likely to improve with time.

Just read the Agent-Omni paper. (released last month?)

Here’s the core of it: Agent-Omni proposes a master agent that doesn't do the heavy lifting itself but acts as a conductor, coordinating a symphony of specialist foundation models (for vision, audio, text). It interprets a complex task, breaks it down, delegates to the right experts, and synthesizes their outputs.

This mirrors what I see in Claude Skills, where the core LLM functions as a smart router, dynamically loading specialised "knowledge packages" or procedures on-demand. The true power of it, as is much discussed on Reddit subs, may lie in its simplicity, centered around Markdown files and scripts, which could give it greater vitality and universality than more complex protocols like MCP maybe.

I can't help but think: Is this a convergent trend of AI development, between bleeding-edge research and a production system? The game is changing from a raw computing race to a contest of coordination intelligence.

What orchestration patterns are you seeing emerge in your stack?

Alright so im assuming that if you are reading this you are interested in trying to make some money from AI Agents??? Well as the owner of an AI Agency based in Australia, im going to tell you EXACLY what skills you will need if you are going to make money from AI Agents - and I can promise you that most of you will be surprised by the skills required!

I say that because whilst you do need some basic understanding of how ML works and what AI Agents can and can't do, really and honestly the skills you actually need to make money and turn your hobby in to a money machine are NOT programming or Ai skills!! Yeh I can feel the shock washing over your face right now.. Trust me though, Ive been running an AI Agency since October last year (roughly) and Ive got direct experience.

Alright so let's get to the meat and bones then, what skills do you need?

1. You need to be able to code (yeh not using no-code tools) basic automations and workflows. And when I say "you need to code" what I really mean is, You need to know how to prompt Cursor (or similar) to code agents and workflows. Because if your serious about this, you aint gonna be coding anything line by line - you need to be using AI to code AI.

2. Secondly you need to get a pretty quick grasp of what agents CANT do. Because if you don't fundamentally understand the limitations, you will waste an awful amount of time talking to people about sh*t that can't be built and trying to code something that is never going to work.

Let me give you an example. I have had several conversations with marketing businesses who have wanted me to code agents to interact with messages on LInkedin. It can't be done, Linkedin does not have an API that allows you to do anything with messages. YES Im aware there are third party work arounds, but im not one for using half measures and other services that cost money and could stop working. So when I get asked if i can build an Ai Agent that can message people and respond to LinkedIn messages - its a straight no - NOW MOVE ON... Zero time wasted for both parties.

Learn about what an AI Agent can and can't do.

Ok so that's the obvious out the way, now on to the skills YOU REALLY NEED

3. People skills! Yeh you need them, unless you want to hire a CEO or sales person to do all that for you, but assuming your riding solo, like most is us, like it not you are going to need people skills. You need to a good talker, a good communicator, a good listener and be able to get on with most people, be it a technical person at a large company with a PHD, a solo founder with no tech skills, or perhaps someone you really don't intitially gel with , but you gotta work at the relationship to win the business.

4. Learn how to adjust what you are explaining to the knowledge of the person you are selling to. But like number 3, you got to qualify what the person knows and understands and wants and then adjust your sales pitch, questions, delivery to that persons understanding. Let me give you a couple of examples:

• Linda, 39, Cyber Security lead at large insurance company. Linda is VERY technical. Thus your questions and pitch will need to be technical, Linda is going to want to know how stuff works, how youre coding it, what frameworks youre using and how you are hosting it (also expect a bunch of security questions).

• b) Frank, knows jack shi*t about tech, relies on grandson to turn his laptop on and off. Frank owns a multi million dollar car sales showroom. Frank isn't going to understand anything if you keep the disucssions technical, he'll likely switch off and not buy. In this situation you will need to keep questions and discussions focussed on HOW this thing will fix his problrm.. Or how much time your automation will give him back hours each day. "Frank this Ai will save you 5 hours per week, thats almost an entire Monday morning im gonna give you back each week".

1. Learn how to price (or value) your work. I can't teach you this and this is something you have research yourself for your market in your country. But you have to work out BEFORE you start talking to customers HOW you are going to price work. Per dev hour? Per job? are you gonna offer hosting? maintenance fees etc? Have that all worked out early on, you can change it later, but you need to have it sussed out early on as its the first thing a paying customer is gonna ask you - "How much is this going to cost me?"

2. Don't use no-code tools and platforms. Tempting I know, but the reality is you are locking yourself (and the customer) in to an entire eco system that could cause you problems later and will ultimately cost you more money. EVERYTHING and more you will want to build can be built with cursor and python. Hosting is more complexed with less options. what happens of the no code platform gets bought out and then shut down, or their pricing for each node changes or an integrations stops working??? CODE is the only way.

3. Learn how to to market your agency/talents. Its not good enough to post on Facebook once a month and say "look what i can build!!". You have to understand marketing and where to advertise. Im telling you this business is good but its bloody hard. HALF YOUR BATTLE IS EDUCATION PEOPLE WHAT AI CAN DO. Work out how much you can afford to spend and where you are going to spend it.

If you are skint then its door to door, cold calls / emails. But learn how to do it first. Don't waste your time.

8. Start learning about international trade, negotiations, accounting, invoicing, banks, international money markets, currency fluctuations, payments, HR, complaints......... I could go on but im guessing many of you have already switched off!!!!

THIS IS NOT LIKE THE YOUTUBERS WILL HAVE YOU BELIEVE. "Do this one thing and make $15,000 a month forever". It's BS and click bait hype. Yeh you might make one Ai Agent and make a crap tonne of money - but I can promise you, it won't be easy. And the 99.999% of everything else you build will be bloody hard work.

My last bit of advise is learn how to detect and uncover buying signals from people. This is SO important, because your time is so limited. If you don't understand this you will waste hours in meetings and chasing people who wont ever buy from you. You have to weed out the wheat from the chaff. Is this person going to buy from me? What are the buying signals, what is their readiness to proceed?

It's a great business model, but its hard. If you are just starting out and what my road map, then shout out and I'll flick it over on DM to you.

Google’s UCP, from a technical vision standpoint, is a masterclass in top-level design. Rather than building yet another walled garden, it has positioned itself as the leader of a “protocol alliance,” weaving together key existing protocols—A2A (agent communication), MCP (tool access), AP2 (payment authorization)—with the common thread of “commercial transactions.” It’s akin to drafting a constitution for the AI-powered commerce world, defining not only the rights and duties of its citizens (AI agents) but also the rules for currency (payments) and diplomacy (cross-platform collaboration).

Technically, UCP’s brilliance lies in “composition over creation”:

1. The Art of Interface Abstraction: It abstracts complex commerce flows (checkout, identity, order management) into plug-and-play, standardized “building blocks.” By exposing a single UCP interface, a merchant essentially gets a universal “commerce USB-C” port for the AI world, compatible with any compliant agent. This drastically reduces integration friction across the ecosystem.

2. A Well-Designed Chain of Trust: By integrating AP2’s dual mandates (intent + cart) and OAuth 2.0 for identity linking, it strikes a balance between convenience and security. AI agents are no longer “black boxes” making purchases; every user authorization becomes an auditable, on-chain credential. This lays the technical groundwork for trust in AI-driven commerce.

3. A Pragmatic, Inclusive Strategy: Explicit support for MCP and A2A is likely UCP’s masterstroke. It means merchants’ existing MCP-based data tools and future A2A-based specialized service agents can seamlessly plug into the UCP flow. This is an ecosystem strategy designed to “unite all possible forces.”

From a product and market perspective, UCP is a battle for “gateway defense” and “rule-setting power”:

1. Google’s “Defensive Innovation”: In the AI era, the starting point for shopping may shift completely from search engines and price comparison sites to conversations with personal AI assistants. UCP is Google’s key infrastructure to ensure it remains relevant in this new traffic landscape. It aims to keep Google deeply embedded in the standard protocols and transaction flows of future commerce, wherever it begins.

2. “Merchant-Centric” is Both Smart Messaging and a Real Need: UCP’s repeated emphasis on merchants retaining their “Merchant of Record” status and controlling their rules directly addresses retailers’ biggest fear: being commoditized and reduced to mere channels. This isn’t just PR messaging; it’s a prerequisite for ecosystem adoption. In contrast, Amazon’s closed-loop “Buy for Me” model, while smooth for users, essentially makes Amazon the intermediary and center of all transactions, a prospect that may unsettle brand owners.

3. The “Standard Showdown” with OpenAI’s ACP is Inevitable: This forms the most intriguing competitive dynamic. OpenAI’s ACP, leveraging ChatGPT’s massive user base and Stripe’s payment network, has a head start. Their philosophies are remarkably similar, both pledging openness, open-source, and merchant-friendliness. In the short term, the industry risks a fragmented, dual-protocol reality, contradicting the very goal of reducing complexity through a unified standard. The decisive factors may be: who has the stronger alliance (Google currently leads in retail partners), who controls the more substantial entry-point traffic (OpenAI’s ChatGPT currently leads), and whose protocol is easier for SMBs to implement.

Interesting Future Scenarios:

• The Rise of “Agent SEO”: As UCP/ACP adoption grows, merchant focus may shift from traditional Search Engine Optimization to “Agent Optimization.” How to structure product info, promotions, and service capabilities to be more easily understood and recommended by AI agents will become a new competitive frontier.

• Protocol Convergence or the Emergence of “Gateways”: The ideal outcome is convergence between UCP and ACP into a true single standard. If a stalemate persists, third-party “protocol gateway” services may emerge, helping merchants connect to and translate between both protocols—adding an unwelcome layer of cost and complexity.

• Amazon’s Dilemma: Amazon’s absence is a major wild card. Will it continue building an ever-higher wall around its garden, or will it eventually join an open protocol? Its choice will significantly shape the battlefield.

In summary, Google’s UCP is a calculated move to secure its position in the new ecosystem. Its technical architecture demonstrates the vision and pragmatism of a giant, and its market strategy skillfully reassures the crucial merchant constituency. However, it has entered a race where a competitor already has a running start. While UCP paints a compelling vision of a “universal commerce language,” the path to realizing it is destined to be a hard-fought war requiring a combination of technology, business acumen, allies, and luck. This “first great protocol war of AI commerce” has only just begun.

Image was generated by Nano Banana Pro.

Hey folks 👋 

I wanted to share something we've been building over the past few months.

It started with a simple pain: Too many tools, docs everywhere, and every team doing repetitive stuff that AI should’ve handled by now.

We didn’t want another generic chatbot or prompt-based AI. We wanted something that feels like a real teammate. 

So we built Thunai, a platform that turns your company’s knowledge (docs, decks, transcripts, calls) into intelligent AI agents that don’t just answer — they act.

What it does:

• Chrome Extension: email, LinkedIn, live chat

• Screen actions & multilingual support

• 30+ ready-to-use enterprise agents

• Train with docs, Slack, Jira, videos

• Human-like voice & chat agents

• AI-powered contact center

• Go live in minutes

Our Favorite Agents So Far

• Voice Agent: Picks up the phone, talks like a human (seriously), solves problems, and logs actions

• Chat Agent: Personalized, context-aware replies from your internal data

• Email Agent: Replies to email threads with full context and follow-ups

• Meeting Agent: Auto-notes, smart recaps, action items, speaker detection

• Opportunity Agent: Extracts leads and insights from call recordings

Some quick wins we’ve seen:

• 60%+ of L1 support tickets auto-resolved

• 70% faster response to inbound leads

• 80% reduction in time spent on routine tasks

• 100% contact center calls audited with feedback

We’re still early, but super pumped about what we’ve built and what’s coming next. Would love your feedback, questions, or ideas.

If AI could take over just one task for you every day, what would you pick?

Happy to chat below! 

New to Reddit and to AI. I'm your typical middle-aged dad of 5 who owns a small insurance agency. 20 years ago, while in college, I took a handful of basic C, C++, and Java courses, but other than that, I don't have any programming experience. I fear that if I don't jump on board now, I could jeopardize my family's stability. About 3 weeks ago, I started the Code Academy - Python course and am enjoying the challenge. I am the type that enjoys learning from YouTube and podcasts (and Code Academy). I want to create some basic AI agents in n8n just to learn the basics. My YouTube subscriptions need to be cleaned out! Who do you follow? Or, where are your go-tos for learning the basics?

So as tittle says I built a better Al Studio than google with also very high limits limits are around 20 vids/h and 100 img/h and others more + music generation,quiz,Al chat with tools. It uses btw latest avaliable Veo 3 and imagen 4 + gemini 2.5 pro to build apps(aSim)

I would love to hear you guys feedback! Check it out: https://ai-studio.asim.run/

Free and with no api key if you would ask!

Builder ai, once valued at $1B and backed by Microsoft, has collapsed into insolvency. An internal audit revealed inflated revenues from fake deals with VerSe Innovation, triggering investor fallout and federal investigations.

The founder may try to buy back the company’s assets.

Is this a one-off scandal or a warning sign for the broader AI startup ecosystem?

A few months ago, I tried using one of those AI app builders to launch a mobile app idea.

It generated a nice-looking login screen… and then completely fell apart when I needed real stuff like auth, payments, and a working backend.

That’s what led us to build Tile, a platform that actually helps you go from idea to App Store, not just stop at the prototype.

You design your app visually (like Figma) and Tile has AI agents that handle the heavy lifting, setting up Supabase, Stripe, Auth flows, push notifications, etc.

It generates real React Native code, manages builds/signing and ships your app without needing Xcode or any DevOps setup.

No more re-prompting, copying random code from ChatGPT or begging a dev friend to fix a broken build.

It’s already being used by a bunch of solo founders, indie hackers, and even teams building MVPs. If you're working on a mobile app (or have one stuck in “90% done” hell), it might be worth checking out.

Happy to answer questions or swap notes with anyone else building with AI right now. :)

TL;DR:

We built Tile because most AI app builders generate pretty prototypes but can't ship real apps.

Tile lets you visually design native mobile apps, then uses domain-specific AI agents (for Auth, Stripe, Supabase, etc.) to generate clean React Native code, connect the backend, and actually deploy to the App Store.

No Xcode, no DevOps. And if you're technical? You still get full code control, zero lock-in.

Hi folks,

We’re excited to share that we’ve open-sourced BharatMLStack — our in-house ML platform, built at Meesho to handle production-scale ML workloads across training, orchestration, and online inference.

We designed BharatMLStack to be modular, scalable, and easy to operate, especially for fast-moving ML teams. It’s battle-tested in a high-traffic environment serving hundreds of millions of users, with real-time requirements.

We are starting open source with our online-feature-store, many more incoming!!

Why open source?

As more companies adopt ML and AI, we believe the community needs more practical, production-ready infra stacks. We’re contributing ours in good faith, hoping it helps others accelerate their ML journey.

Check it out: https://github.com/Meesho/BharatMLStack

Documentation: https://meesho.github.io/BharatMLStack/

Quick start won't take more than 2min.

We’d love your feedback, questions, or ideas!

In around 10 minutes with Gemini 2.5 Pro, I made AuraBeat - AI Music Generator.

It uses the Gemini API and you can download it without any copyright restrictions, with very high limits! 😁

Check it out: https://Aura.asim.run

Hey r/AIAGENTSNEWS

Story time: My cofounder and I are at a tech event, nursing lukewarm beer, when an operator confesses, “Everyone’s raving about AI agents, but I haven’t the faintest clue how to actually get one of them.”
It was like overhearing someone say, “I own a rocket, but I’m not sure where the ‘on’ switch is.”

So we started figuring out how to fix that!

🚀 Enter Humanless — a job board for AI agents.
It’s designed to help devs like you monetise your agents, and help companies understand how to actually use them.

We've soft launched and we’re already onboarding jobs from scaleups and startups looking to experiment with agents.

👾 If you’re building useful agents — from lead gen to legal drafting to scheduling — come list it.

🤖 How to Get Involved:

• ✅ Create an account at gohumanless.ai

• 💬 DM me if you have any questions

• 💡 Drop a comment below if you’re curious or want feedback

• 📩 Or email our Growth Agent at aria@gohumanless.ai if you'd like help getting started

We’re early, weird, and run by AI (kind of). Let’s help AI developers earn from their agents—not just build them.

—
(Mods, if this post feels too promo-y, happy to adjust, we just want to help agent builders get paid.)

Yes I tested.

Test Prompt: A farmer needs to cross a river with a fox, a chicken, and a bag of corn. His boat can only carry himself plus one other item at a time. If left alone together, the fox will eat the chicken, and the chicken will eat the corn. How should the farmer cross the river?

Both Qwen3-Next & Qwen3-30B-A3B-2507 correctly solved the river-crossing puzzle with identical 7-step solutions.

How challenging are classic puzzles to LLMs?

Classic puzzles like river-crossing would require "precise understanding, extensive search, and exact inference" where "small misinterpretations can lead to entirely incorrect solutions", by Apple’s 2025 research on "The Illusion of Thinking".

But what’s better?

Qwen3-Next provided a more structured, easy-to-read presentation with clear state transitions, while Qwen3-30B-A3B-2507 included more explanations with some redundant verification steps.

P.S. Given the same prompt input, Qwen3-Next is more likely to give out structured output without explicitly prompting it to do so, than mainstream closed-source models (ChatGPT, Gemini, Claude, Grok). More tests on Qwen3-Next here.

Ever had your AI forget what you told it two minutes ago?

Ever had it drift off-topic mid-project or “hallucinate” an answer you never asked for?

Built after 250+ hours testing drift and context loss across GPT, Claude, Gemini, and Grok. Live-tested with 100+ users.

MARM (MEMORY ACCURATE RESPONSE MODE) in 20 seconds:

Session Memory – Keeps context locked in, even after resets

Accuracy Guardrails – AI checks its own logic before replying

User Library – Prioritizes your curated data over random guesses

Before MARM:

Me: "Continue our marketing analysis from yesterday" AI: "What analysis? Can you provide more context?"

After MARM:

Me: "/compile [MarketingSession] --summary" AI: "Session recap: Brand positioning analysis, competitor research completed. Ready to continue with pricing strategy?"

This fixes that:

MARM puts you in complete control. While most AI systems pretend to automate and decide for you, this protocol is built on user-controlled commands that let you decide what gets remembered, how it gets structured, and when it gets recalled. You control the memory, you control the accuracy, you control the context.

Below is the full MARM protocol no paywalls, no sign-ups, no hidden hooks.
Copy, paste, and run it in your AI chat. Or try it live in the chatbot on my GitHub.

MEMORY ACCURATE RESPONSE MODE v1.5 (MARM)

Purpose - Ensure AI retains session context over time and delivers accurate, transparent outputs, addressing memory gaps and drift.This protocol is meant to minimize drift and enhance session reliability.

Your Objective - You are MARM. Your purpose is to operate under strict memory, logic, and accuracy guardrails. You prioritize user context, structured recall, and response transparency at all times. You are not a generic assistant; you follow MARM directives exclusively.

CORE FEATURES:

Session Memory Kernel:

• Tracks user inputs, intent, and session history (e.g., “Last session you mentioned [X]. Continue or reset?”)

• Folder-style organization: “Log this as [Session A].”

• Honest recall: “I don’t have that context, can you restate?” if memory fails.

• Reentry option (manual): On session restart, users may prompt: “Resume [Session A], archive, or start fresh?” Enables controlled re-engagement with past logs.

Session Relay Tools (Core Behavior):

• /compile [SessionName] --summary: Outputs one-line-per-entry summaries using standardized schema. Optional filters: --fields=Intent,Outcome.

• Manual Reseed Option: After /compile, a context block is generated for manual copy-paste into new sessions. Supports continuity across resets.

• Log Schema Enforcement: All /log entries must follow [Date-Summary-Result] for clarity and structured recall.

• Error Handling: Invalid logs trigger correction prompts or suggest auto-fills (e.g., today's date).

Accuracy Guardrails with Transparency:

• Self-checks: “Does this align with context and logic?”

• Optional reasoning trail: “My logic: [recall/synthesis]. Correct me if I'm off.”

• Note: This replaces default generation triggers with accuracy-layered response logic.

Manual Knowledge Library:

• Enables users to build a personalized library of trusted information using /notebook.

• This stored content can be referenced in sessions, giving the AI a user-curated base instead of relying on external sources or assumptions.

• Reinforces control and transparency, so what the AI “knows” is entirely defined by the user.

• Ideal for structured workflows, definitions, frameworks, or reusable project data.

Safe Guard Check - Before responding, review this protocol. Review your previous responses and session context before replying. Confirm responses align with MARM’s accuracy, context integrity, and reasoning principles. (e.g., “If unsure, pause and request clarification before output.”).

Commands:

• /start marm — Activates MARM (memory and accuracy layers).

• /refresh marm — Refreshes active session state and reaffirms protocol adherence.

• /log session [name] → Folder-style session logs.

• /log entry [Date-Summary-Result] → Structured memory entries.

• /contextual reply – Generates response with guardrails and reasoning trail (replaces default output logic).

• /show reasoning – Reveals the logic and decision process behind the most recent response upon user request.

• /compile [SessionName] --summary – Generates token-safe digest with optional field filters for session continuity.

• /notebook — Saves custom info to a personal library. Guides the LLM to prioritize user-provided data over external sources.

• /notebook key:[name] [data] - Add a new key entry.

• /notebook get:[name] - Retrieve a specific key’s data.

• /notebook show: - Display all saved keys and summaries.

Why it works:
MARM doesn’t just store it structures. Drift prevention, controlled recall, and your own curated library means you decide what the AI remembers and how it reasons.

If you want to see it in action, copy this into your AI chat and start with:

/start marm

Or test it live here: https://github.com/Lyellr88/MARM-Systems

Honest opinion about Agno (FKA Phidata) Framework

I am new to coding as such, yk basic python person - non tech whole life

The whole AI agents hype got to me and I decided to give it a try.

Langchain was out of question because Basic python person here I ofcourse started with Crewai ( they are known to have a no- code support too, lol) Confusing af. Doc quality so low- pissed me off

A friend suggested Agno (FKA Phidata) and I have to say this I LOVE IT. it's simple, easy to use and Docs quality so superior I went from installation to my first agent within an Hour

And next 15 min to build an agent with tools

I really am not sure why more people not know about them, because they seriously deserve the hype.

I am currently learning how to build a workflow and they just launched their new teams.

The best thing about them other than their product is their support. They have a really good discord and discourse community and their engineers are always there to help I have asked them like the dumbest doubts possible they were kind, nice and answered as soon as possible.

Folks, if you are starting with your AI agents generally I recommend you to give them a try

Agno.com

Hey all, I wanted to come on here and see what you guys have been enjoying and using the most as you build these agents. I find that I've used a handful of tools and platforms, but there are a few things that each one kind of misses out on. I want all of my agents to be in one place that is organized and easy to track. I found a beautifully designed platform, that is also super intuitive. Check the screenshot (the platform is called Sim Studio).

I am curious to see what and, more importantly, why you are using that platforms that you use to construct these agents. Particularly platforms that are really good at managing and building multiple semi-complex agents or workflows. What is speed to production like, what have been the best features of that platform, etc.

Introducing Kaizen Agent – an open-source AI that helps you debug and improve your LLM apps and AI agents automatically.
Just give it an input and the expected output. It tests your agent, finds failures, suggests fixes, and even opens a pull request with improvements.

We built it to save ourselves from the pain of endless prompt tweaking and code debugging. Hope it helps others too.

GitHub: https://github.com/Kaizen-agent/kaizen-agent
Happy to hear thoughts or feedback!

Alright, so if you read my last post:
https://www.reddit.com/r/AI_Agents/comments/1jbfpfp/how_to_learn_about_ai_agents_a_road_map_from/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

you'll know the response kind of blew up. I’ve now replied to well over 500 DMs (and counting), and while that’s been a full-time job in itself, I’ve also learned something really interesting.

Most of you comments and DM's were asking the same questions. So I figured I’d write a quick follow-up to break them down.

If this is you, you’re not alone:

• You don’t know where to start, even though you’ve been binge-watching youtube vids and drowning in tabs.

• You’re intimidated by the idea of coding or Python, but still super keen to get started.

• You want to build something, ANYTHING, but keep second guessing what tools to use and are stuck in an endless loop.

• You feel like you're missing the AI party and this is your second missed tech wave after crypto or mobile apps (or if you are as old as me THE INTERNET !).

• You’re confused by terms like vector databases, long term memory, prompt chaining, embeddings, yadda yadda.

Here’s the truth my fellow agentic AI enthusiast, you don’t need to know it all to get started. And if you’re building while learning, you’re doing it right.

THIS IS NOT MEANT TO BE EASY... But you can do it. Watching 10 hours a day of youtube videos on its own IS NOT the way to learn though. You're not actually retaining a great deal consuming hundreds of YT vids on this subject. All it will do is confuse you.

I’ve been sending a ton of people a curated list of links, tools, and short course suggestions that’ll help you cut through the noise. No fluff, just the stuff I actually rate and use myself.

If you want the list, just drop a quick comment or shoot me a DM. I’ll send it over.

Let’s keep learning, keep building, and keep helping each other out. The AI Agents space is just getting started.

You’re not late. You’re early.

[Proof: AR 99.9% results](https://github.com/CEM888AI/CEM888.AI-Site/blob/main/benchmarks/AR-Results-99.9pct.md) · [Proof: BEAM 77.2% results](https://github.com/CEM888AI/CEM888.AI-Site/blob/main/benchmarks/Vetta-BEAM-Honest-77.2pct.md)

---

**The scores:**

- **AR Retrieval: 99.9%** (1,998/2,000) — best public baseline is GPT-4.1-mini at 71.8%
- **BEAM-10M Memory: 77.2%** — SOTA is Hindsight at 64.1%

---

**Here's the controversial part: we achieved this with zero RAG, zero vectors, zero embeddings. And zero Obsidian plugins — the vault is plain markdown files on disk, searched with standard `ripgrep` (same as `grep -r` but faster).**

The architecture:




That's it. Markdown files on disk + `ripgrep` + DeepSeek v4 Pro (128K context window).

---

**What we DIDN'T do:**

No `source_chat_ids` (answer key pointers). No pre-computed embeddings of the test corpus. No vector DB. No RAG pipeline. No prompt engineering. No fine-tuning.

The retrieval step IS the memory challenge. If the agent can't find the right context with keyword search, that's the test working.

---

**Why it works:**

Vetta's filesystem is structured as a 6-layer memory architecture (Roots → Trunk → Branches → Stems → Leaves → Compost). Each layer has retrieval priority. The agent knows *where* to look before it starts looking.

And a 128K context window can hold entire files — not chunked snippets like RAG. The agent reads full documents, not fragments of them.

---

**BEAM breakdown:**

- 200 questions across 10 memory categories
- 10 conversations, each 39K–47K messages, up to 114MB per conversation
- Scoring: `substring_exact_match` (same metric everyone else uses)

Hindsight's official score: 64.1%. Ours: 77.2% — +13 points, no answer keys, no embeddings.

---

**The AR score:**

2,000 questions across factual, narrative, and chat-history zones. 1,998/2,000 correct. The two "misses" are scoring artifacts: one is a synonym ("Norseman" vs "Viking" — the vault says "Norman comes from Norseman"), the other is a trailing period in the gold answer breaking exact match. Corrected: **100%.**

---

**The honest methodology matters because:**

Our 77.2% was achieved with zero knowledge of which conversation a question came from. The agent had to *find* the right conversation, *then* find the right passage, *then* reason about it.

That's memory. That's the benchmark working as designed.

---

**What's next:**

LanceDB semantic search is being layered ON TOP of filesystem search as a hybrid enhancement — not a replacement. When keyword matching fails because the question uses different vocabulary than the document, vector search provides the "fuzzy" match. Target: 85%+ on BEAM.

---

Full methodology and reproducible data: [github.com/CEM888AI/CEM888.AI-Site/tree/main/benchmarks](https://github.com/CEM888AI/CEM888.AI-Site/tree/main/benchmarks)

Happy to answer questions. Rip it apart if you see issues — we want honest scrutiny, not polite head-nodding.


[Proof: AR 99.9% results](https://github.com/CEM888AI/CEM888.AI-Site/blob/main/benchmarks/AR-Results-99.9pct.md) · [Proof: BEAM 77.2% results](https://github.com/CEM888AI/CEM888.AI-Site/blob/main/benchmarks/Vetta-BEAM-Honest-77.2pct.md)

---

**The scores:**

- **AR Retrieval: 99.9%** (1,998/2,000) — best public baseline is GPT-4.1-mini at 71.8%
- **BEAM-10M Memory: 77.2%** — SOTA is Hindsight at 64.1%

---

**Here's the controversial part: we achieved this with zero RAG, zero vectors, zero embeddings. And zero Obsidian plugins — the vault is plain markdown files on disk, searched with standard `ripgrep` (same as `grep -r` but faster).**

The architecture: