@iBoomto

Turn documents into finished work. Online AI tools for a single file. iBoomto AI, a Windows AI document agent, when the work runs across files and steps.

[@iBoomto @iboomto_ai]

Then you're twisting it into whatever you actually needed it for. The reading is fast. It’s the endless scrolling and re-finding that eats the whole afternoon.

[@iBoomto @iboomto_ai]

Keep noticing this: when you open a massive doc to pull an answer, you aren't really reading. You skim. You spot a chunk that looks right, stop, and then you're scrolling back up three pages to check it against something else.

[@iBoomto @iboomto_ai]

You surface three answers, copy them into a note, and you're still not sure you caught every date.

That's the job hiding inside "real quick." It's not one task. It's a scavenger hunt with worse lighting.

[@iBoomto @iboomto_ai]

"Grab the deliverables real quick" is never quick. Here's where the time actually goes.

The partnership deck runs 40 pages, heavy on mockups and lookbook shots. It takes a second just to open.

You need three things: payment terms, campaign dates, what you owe.

[@iBoomto @iboomto_ai]

Search doesn't save you. Every section words it differently, so you scroll by eye. One page is an image (search skips it entirely).

The reading was never the slow part. The finding was.

[@iBoomto @iboomto_ai]

Reading it was the easy bit. So that's what we're building for: the messy middle.

Everything between opening a file and calling it done.

[@iBoomto @iboomto_ai]

Quick story on why iBoomto exists.

A few months back, one of us summarized the same 90-page vendor contract for the third time in a month.

[@iBoomto @iboomto_ai]

Nothing in it had changed. We just kept redoing the summary in whatever tool was open, and copying it from one to the next is what quietly killed the afternoon.

[Vivi @vivistac]

Anthropic ha lanzado una guía de 37 minutos para construir Agentes de IA que automatizan una empresa entera.

Gratis. De los ingenieros que construyeron Claude.

Agentes que trabajan, se reparten tareas y ejecutan todo solos.

Subtitulado al español.

Guárdate este post. 🔖

[Argona @Argona0x]

this is worth more than most five figure courses

16 claude agents audit an entire repo at once, a second fleet re-checks every finding on fresh context, and the whole thing runs off one diagram instead of a prompt

i ran it against my own code and got back 11 endpoints where i never checked who was logged in, 3 of which the verifier threw out before they ever reached me

this is Graph Engineering, the layer above prompting, and it runs on the agent you already pay for:

- write your plan out, then ask one question at every "and then": does the next step actually read what the previous one produced
- the seams that fail that question were never dependencies, so those jobs run at the same time
- the arrows that survive are your real edges, and the longest chain of them is your floor that no number of agents shortens
- want it faster, cut a false edge instead of adding a worker
- fan the independent work out, one agent per item, no shared state between them
- send every finding to a separate agent on fresh context, because a model recognises its own writing 73.5% of the time and grades it kinder once it does
- make that verifier check a real signal like a passing test, never the worker's own word that it finished
- shard the fleet across worktrees so parallel workers stop overwriting each other, one rule frozen into every worker: never git stash, never git reset
- merge only what came back verified, into one report instead of twenty open chats

the catch is the ceiling. at 95% independent work 16 agents return 9.14x rather than the 16 you would guess, and even 256 only reach 18.6x, because the merge and the verify stay serial however wide you fan

coordination itself is free plain code and every agent underneath it is billed, so start at twenty files and widen once it works

bookmark this, the whole method with all six ready-to-run graphs is written out in the article ↓

[Alvaro Cintas @dr_cintas]

your AI agent can watch any video now - paste a URL and it sees every frame, hears every word, all for free 🤯

bradautomates/claude-video gives Claude the ability to watch YouTube, Loom, TikTok, local files - anything yt-dlp supports

what people actually use it for:

→ analyze a competitor launch - what hook, what visuals, what structure
→ debug from a screen recording - Claude reads the exact frame where it breaks
→ summarize a 49-min talk in 30 seconds with frame-accurate timestamps
→ strip the hype from product videos - "what's actually new, skip the pitch"

the mechanism: yt-dlp pulls free captions first (zero cost). ffmpeg extracts frames at scene-aware intervals - not uniform sampling, so you don't waste tokens on 12 identical frames of the same slide. Claude reads every frame as an image with timestamp markers. Groq Whisper only kicks in when a video has no caption track

how to set up (3 min):

> claude code: /plugin marketplace add bradautomates/claude-video then /plugin install watch@claude-video
> or npx skills add bradautomates/claude-video -g for codex, cursor, gemini cli
> dependencies auto-install on macOS via brew

two caveats: free captions cover most but not all videos. past 10 min use --start/--end for focused sections or the token-burner mode for full coverage

your buddy still watches every tutorial at 2x speed taking manual notes. you paste a URL and your agent extracts the substance in seconds for $0

[Peter Yang @petergyang]

I’m sick of reading AI slop, so today I’m open-sourcing my /no-ai-slop skill that removes 20+ slop patterns from any piece of writing.

📌 Get the free skill here: https://t.co/YshUga82ks

If you find it useful, please consider starring the repo so more people can find it.

Why I built the skill:

I use AI to edit my writing because it helps me fix spelling, grammar, and clarity.

But even the best models keep producing the same slop that this skill removes:

→ Binary contrasts: “It’s not X. It’s Y.”
→ Throat-clearing openers: “Here’s what nobody tells you.”
→ Fake-profound endings: “The future isn’t coming. It’s already here.”

Use this skill responsibly.

I always write a first draft manually before iterating with AI on edits and I make sure to do another manual pass at the end as well.

That’s in contrast to using AI to automate pumping out slop end-to-end.

📌 Read my full post for more on how I try to use AI responsibly to edit without giving into the dark side: https://t.co/C68RSabtb0

[Google Gemma @googlegemma]

Voice AI without the wait! ⏱️

Thanks to Hugging Face and Cerebras, developers can now use the Gemma 4 31B model as the brain for voice AI at ultra-fast inference speeds. Add it to a fully open-source, cascaded speech-to-speech stack that can be used to power existing voice apps! 🗣️

[Hanako @hanakoxbt]

6 agent patterns for AI engineers:

(explained with usage)

1) prompt chaining
→ split the task into fixed steps, each one checking the last.
→ use when the task decomposes cleanly and accuracy matters more than latency.

2) routing
→ classify the input first, then send it to the model or tool built for it.
→ use when inputs fall into distinct classes that need different handling.

3) parallelization
→ run several calls at once and merge them, either by splitting the work or voting on the same question.
→ use when subtasks are independent, or when one answer deserves several opinions.

4) orchestrator-workers
→ a lead model decides what the subtasks are at runtime, then delegates them.
→ use when you cannot list the steps in advance.

5) evaluator-optimizer
→ one model writes, another grades, the loop repeats until it passes.
→ use when you have clear criteria and iteration measurably helps.

6) autonomous agent
→ no fixed path. it plans, acts, reads feedback from the environment, and decides when it is done.
→ use when the steps are unknowable and you can afford the cost and the blast radius.

the first five are workflows: you wrote the path. only the last one writes its own.

most production systems people call agents are pattern 1, 2, or 5 with good error handling.

this taxonomy is from Anthropic's own writeup on building effective agents.

full breakdown in the article below.

[@iBoomto @iboomto_ai]

good looking animations

[thijs @cdngdev]

i gave 5.6 sol access to my camera roll and had it extract pictures of every piece of clothing i own from my photos

then, told it to find new outfits for me and render them on me with gpt-image!

its kinda cool to see your entire wardrobe in a collection like this

[Cartesian Caramel @Cartesian_C]

Printing Hand Muscles Test #b3d #geometrynodes

[Google Cloud @googlecloud]

Virgin Voyages set sail with Google Cloud and saw a:

- 60% reduction in productions timeline
- 28% increase month on month for a record sales year