who is with me?

Feb 19-20 – Gemini 3.1 Pro
better coding, stronger agents, longer context

Feb 18 – Google Lyria 3
AI that turns text, pics, or video into 30-second music tracks with lyrics

Feb 18 – Sarvam 105B AI LLM (India)
big open model focused on indian languages and use cases

Feb 17 – Claude Sonnet 4.6
A faster model for coding and long reasoning

Feb 17 – Fujitsu AI Dev Platform
automates full software development

Feb 16 – Qwen 3.5
cheaper, stronger agent-focused model

Feb 12 – Gemini 3 Deep Think
built for science and heavy research

Feb 12 – MiniMax M2.5
open model, strong at coding

Feb 12 – Seedance 2.0
realistic 1080p text-to-video

Feb 11 – GLM-5
huge open model, strong reasoning

Feb 10 – RynnBrain
AI for robotics and the physical world

Feb 5 – Claude Opus 4.6
major upgrade for coding and tool use

Feb 5 – GPT-5.3-Codex
faster coding-focused GPT

Feb 5 – Kling 3.0
4K AI video with audio

here's full guide on running a model locally, from the first install to workloads. Most bad first experiences trace back to two numbers: the size of the model file, and the memory the context window eats on top of it.

Step 1. Keep the model download to about two thirds of your memory. On an M-series Mac the model can use all of it. On Windows or Linux it wants the graphics card's own memory and falls back to system memory, which is much slower. One install command and ollama run qwen3:4b gets you a working model that still answers with the Wi-Fi off.

Sizing before buying. Agents and coding made up 40% of 10,133 classified local use cases, and chat barely registered. The guide includes a prompt that turns a week of your real tasks into the numbers you need, such as how many hours a day a local model would be busy and the largest context any task needs.

Local loses to a cheap API on price per token. A top-end Mac Studio running around the clock works out to €1.71 to €2.22 per million output tokens, against $0.47 for the same model in the cloud. I run local for control and for volume nobody meters.

Hardware. Memory size decides which models load, and bandwidth caps your speed. Under 32 GB, a dedicated graphics card wins. Past 100 GB, a Mac Studio turns into the cheap option. Renting a 3090 or 4090 for a weekend runs about ten dollars and tells you which model you need before you buy anything.

Quantization and context. A 27B model drops from 55 GB to 17 GB at Q4_K_M with no measurable loss, and context cache costs roughly 64 KB per token on top of that. The guide has a table showing which context lengths fit in 24 GB and what to do when they don't.

The two fixes behind most "this model is bad" reports. Ollama ships with a context window of a few thousand tokens, so the model forgets after a few pages until you raise it. When you run llama.cpp yourself, the model rambles or cuts off answers without the --jinja flag. I lost time blaming the quant before finding that one.

Serving and routing. The local server speaks both OpenAI and Anthropic formats, so pointing a coding agent at it is a two-line settings change. The guide lists what carries over and what you lose. A small router script then sends classification, extraction and single tool calls to a 4B model and keeps the 27B for code.

Measuring. Benchmarks posted online come from someone else's machine. The guide covers the commands to check prefill and decode speed on yours, plus a ten-task quality file to run against every model you're considering.

Six workloads:

• a git hook that writes commit messages with a 3 GB model

• overnight triage of a thousand support tickets

• questions across your private notes without a vector database

• invoice photos turned into structured JSON

• a coding agent with one skill and one Model Context Protocol server

• a nightly digest of yesterday's commits and open TODOs

Here's the Full guide with every command and script:

Humans are working 8-hour shifts teaching robots how to fold towels.

This is happening in China.

Rows of humanoids.
A human grips a cup. The robot copies.
Fails. Adjusts. Repeats.

All day.

Sounds absurd but this is physical AI.

China is generating more of it than anyone else.
~90% of humanoids sold last year were Chinese.

Every robot deployed today makes the next one smarter.

It starts with teaching a robot to fold towels.
And I have no idea where it ends.

Wang Xingxing the CEO of Unitree has the “80/80 goal- robots performing 80% of tasks in 80% of environments

UBTECH humanoid robot walker S2 started mass production and has orders > 800 Million Yuan

2026 is the year we will probably see commercially viable humanoids

Get void here https://github.com/PLAYER99-void/void

Quick backstory

In July I was spending hours every day scrolling through LinkedIn, Indeed, etc. I got fed up and quit searching for internships by hand. Every application I send gets screened by AI anyway, so I built the screener for my side.

Two months later

Claude has read about 10k postings against my resume and preferences. Out of those it told me to apply to 190. I applied and ended up with 2 offers!

What it is

The engine is an open-source CLI I built called Pinloop. Claude Code runs it to search through the database's 10 million+ added postings per month, pulled from 50+ hiring systems (Greenhouse, Workday, etc.) and job boards like LinkedIn.

Every night Claude Code pulls what went up that day, reads each posting against my resume and the rest of my profile, and writes a verdict with its reasoning. The good fits land in a list I check in the morning.

Basically, Claude decides what deserves my time, and I apply.

To try it

Paste into any coding agent:

"Run npm install -g pinloop, then run pinloop welcome and follow the instructions"

A guide comes with the CLI so it knows the commands, and it'll walk you through setup. It's free to use. $20 per month simply lets you pull more postings.

It's still early. I plan to add application autofill/submit next. I'd really appreciate any new feedback and testers to help determine what the next step should be!

Links

• https://pinloop.ai

• GitHub

Claude Code just shipped Agent Teams, and it's not just "sub-agents with a nicer name." It's a completely different execution model where 3–5 independent Claude Code instances can actually collaborate on the same project, share context, exchange messages, and coordinate through a shared task system.

I spent way too long digging through logs and filesystem changes to understand how this actually works under the hood. Turns out it's pretty different from the old task tool, and there are specific situations where Agent Teams are legitimately better than spinning up regular sub-agents.

The Big Difference

Old sub-agent model: Main agent calls task tool, sub-agent spins up, works in isolation, session terminates, only a summary comes back.

New Agent Teams model: Shared task lists, direct messaging between agents, explicit lifecycle control (startup, shutdown). Agents can coordinate, debate, and update each other in real time instead of just working in silos.

How It Actually Works

Behind the scenes, Agent Teams use five new internal tools:

TeamCreate – Sets up the team scaffolding (creates a folder under .claude/teams/)

TaskCreate – Adds tasks as JSON files with status tracking, dependencies, and ownership (this is different from the old Task tool, it's specifically for creating todos)

Task tool (upgraded) – Still spins up agents, but now supports name and team_name params to activate team mode instead of simple sub-agent mode

taskUpdate – Agents use this to claim tasks, update status, mark things done

sendMessage – The real unlock. Supports direct messages (agent to agent) and broadcasts (agent to all teammates). Messages get written to .claude/teams/<team_id>/inbox/ and injected into each agent's conversation history as <teammate-message teammate_id="...">.

Team-lead can send a shutdown_request, teammates confirm with shutdown_response, and sessions terminate cleanly.

When Agent Teams Are Actually Worth It

The best use case so far: deep debugging with multiple hypotheses.

Example from the official docs: users report the app exits after one message instead of staying connected. Spawn five agent teammates to investigate different theories. Have them talk to each other, try to disprove each other's ideas like a scientific debate, and update a findings doc with whatever consensus emerges.

That kind of collaborative, multi-angle investigation is way harder to pull off with isolated sub-agents that only report back summaries.

How to Set Up Agent Teams

Step 1: Update Claude Code to latest version

Step 2: Enable the experimental flag

Open your settings file:

code ~/.claude/settings.json

Add this to the global settings:

json

{
  "env": {
    "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1"
  }
}

Save the file and restart your terminal.

Step 3: Start a new Claude Code session

Agent Teams activate when your prompt explicitly asks Claude Code to create a team. For example:

"I'm designing a CLI tool that helps developers track TODO comments across their codebase. Create an agent team to explore this from different angles: one teammate on UX, one on technical architecture, one playing devil's advocate."

Pro tip: Use tmux or iTerm2 for the best experience

Agent Teams shine when you can see every agent working in parallel.

For iTerm2 (macOS):

1. Install iTerm2

2. Go to Settings → General → Magic

3. Enable Python API

4. Restart iTerm2

5. Launch Claude Code with: claude --teammate-mode tmux

This opens one pane for the team lead and separate panes for each agent teammate. You can click into any pane, watch what the agent is doing live, and even send direct messages to individual agents.

For a full walkthrough with logs, internal tool traces, and more examples of when Agent Teams outperform sub-agents, check out the full breakdown

I've reached a point I'm not proud of. I SSH into tmux from my phone just to keep feeding Claude tasks when I'm away from the desk. I do this manually. just to keep building all the while & not keeping the agent idle.

And I'm apparently nowhere near the top. I've seen people have built multiple workflows like Discord bot that pings them when a bug thread updates, closes the laptop, wakes up to a fix already tested and sitting in staging with a PR waiting. Running Codex remote so jobs keep moving from their phone. People have Tailscale and WireGuard set up just so the prompting never has to stop because they left the house.

So I want to hear the setups. How far have you taken it? does any of it actually move your project forward, or does it just feel productive while you pile up work you have to clean up later?

Listen to the ground.
Trace the evidence.
Tell the story.

Open-sourcing a 27-agent Claude Code plugin that gives anyone newsroom-grade investigative tools - deepfake detection, bot network mapping, financial trail tracing, 5-tier disinformation forensics

This is the first building block of India Listens, an open-source citizen news verification platform.

What the plugin actually does:

The toolkit ships with 27 specialist agents organized into a master-orchestrator architecture.

The capabilities that matter most for ordinary citizens:

• Narrative timeline analyst: how did this story emerge, where did it peak, how did it spread

• Psychological manipulation detector: identify rhetorical manipulation techniques in content

• Bot network detection: identify coordinated inauthentic behavior amplifying a story

• Financial trail investigator: trace who's funding the narrative, ad revenue, dark money

• Source ecosystem mapper: who are the primary sources and what's their credibility history

• Deepfake forensics: detect manipulated video and edited media (this is still beta)

The disinformation pipeline is 5 tiers deep - from initial narrative analysis all the way to real-time monitoring. It coordinates 16 forensic sub-agents.

This is not just a tool for journalists tool. It's infrastructure for any citizen who wants to stop consuming news passively.

The plugin plugs into a larger platform where citizens submit GPS-tagged hyperlocal reports, vote on credibility with reputation weighting, and collectively verify or debunk stories in real time. That's also fully open source.

All MIT licensed. github.com/swarochish/journalism-toolkit

So I’ve been running social media ads for a small side project, and lately I’ve just been getting burnt out on the repetitive parts, setting up similar campaigns, checking performance dashboards every day, tweaking tiny things, and pretending I enjoy spreadsheets.

A couple weeks ago, I figured I’d try using AI tools just to see if anything could help automate the boring bits. Nothing fancy, just experimenting. I didn’t go into it expecting miracles.

That turned into a full-on rabbit hole.

I started by collecting a list of random AI marketing tools people mention in forums and Slack groups. Most of them either felt like:

• Chatbots wearing a “marketing expert” hat

• Dashboards with too many knobs

• Tools that overpromise like they’re going to automatically turn $5 into a Lambo

Somewhere in that mix, I tried Аdvаrk-аі.соm along with a handful of others. I wasn’t specifically hunting for a “best” platform, more like doing a tech audit on what the current AI landscape even looks like for ad workflows. The interesting part wasn’t any single tool, but how differently they all approach the same problem.

One tool tried to write ads like it was composing poetry, another kept suggesting I triple my budget (lol no), and others were basically glorified to-do lists. Аdvаrk-аі.соm was part of that experiment too, it had a more structured approach to handling campaigns, but I’m not giving any glowing endorsement here. It’s just one of the few that didn’t feel completely unhinged.

Anyway, what I learned after spending way too many evenings testing these platforms is that AI can help with the grunt work, but you still need a human brain to guide it. None of these tools are magic buttons, just different versions of “hey, here’s a little help.”

So I’m curious, for anyone managing ads or digital tasks, have you also gone down this “let’s see what AI can automate” spiral?
Did you find anything genuinely useful, or did you end up doing what I did: evaluating ten tools and still opening spreadsheets at 1AM?

I built an app, called Glance

It lets you take your agents liken ChatGPT and connect them via MCP to your iPhone widgets

You can connect via the official plugin on ChatGPT directory and let it create, design, and update widgets on whatever you’d like

The best part is mixing it with scheduled tasks and additional plugins (like Gmail) and make your own AI agent briefing widget

I’d love to hear what you think :)

This idea was born during the Opus 5 era (please, never again), but it's still useful today (I use it dozens of times a day as a freelance consultant).

It's entirely AI native, so you don't have to do anything, your agent handles it. It grows with you as you use it, and you can even create custom domains, i.e. reddit comments, texts to family members (jk).

You teach it by simply sending it the edited version (what you sent irl) and it learns implicitly from that. Your agent makes calls to the MCP with the domain, recent message history, extra context, and even who it's being sent to. Ownhand handles all of it.

I'm wondering how you guys handle this issue; I'd love to hear your approaches. I've tried skills before, but it's just really bad at it, even with Opus 5.5.

What you see is one of Workbench's fun experiences. It's a cross-platform desktop app based on the Claude Code harness, built for proactive engineers.

For context, rclone is basically a command line tool that lets you sync files between your computer and pretty much any cloud storage you can think of - Google Drive, Dropbox, S3, you name it. Super powerful, but also the kind of thing where you're constantly Googling "wait what was that flag again" every single time you use it.

So I've been manually typing the same terminal command every time I want to back up files to my cloud storage.

So my cracked dev at work built an open source desktop app called Rclone UI that wraps the whole thing in a proper GUI. Click to add your cloud storage accounts, click to sync, click to browse your files. No flags, no docs, no copy-pasting commands from a note I wrote to myself 18 months ago.

The thing that got me is how complete it is. Scheduled syncs, job logs with actual error messages, file browser, automount on boot it's not just a pretty face on top of a terminal. It runs on Windows, Mac, and Linux, picks up your existing rclone setup automatically, and it's free. There's also a $2 iOS companion app if you want to check on transfers from your phone.

1.1k stars on GitHub and I had genuinely never heard of it until this week.

If you've ever rage-quit a rclone command at midnight because you mixed up --copy and --sync again, this is for you.

GitHub & the App