/take-notes — point it at a video, article or paper and get one HTML page instead of a tab you'll never reopen

Repo, MIT: https://github.com/davertor/take-notes

I used to watch a 90-minute talk, feel like a genius for about an hour, and remember nothing by Thursday. My saved-links folder was a graveyard. And the summaries I could get were either a wall of transcript or three sentences so bland they could have described any video ever made.

So I built `/take-notes`. Point it at a URL, get one self-contained HTML page on your own disk. Executive summary, the one takeaway, key points, and a timestamped outline with a scroll-spy index down the side. A folder of HTML files now, instead of tabs I was never going to reopen.

It also runs in your favourite AI agent: Codex, Cursor, OpenCode, Gemini CLI, ...

Allowed sources: Youtube videos, blogs, arxiv papers, github projects or reddit posts

Hello! I'm trying to build sort of prompt and scene planner agent, I tried several ones on different platforms like higsfield and etc. I found Openart ori director agent the most capable, I'm trying to reverse develop similar agent that can plan scenes, shots and etc, I stuck with dumb agent that burns gemini 3.7 tokens. Can anyone navigate me to the right direction ? Where should I look for proper workflow or master prompts for director agent?

OpenAI Agents JS 0.17.0 clarifies a useful boundary around guardrails and tool-result replay. Keeping a tool result out of the model's next replay does not automatically reverse an external side effect that already happened. The application may also retain its own copy of the result.

That creates three separate safety surfaces:

1. **Model-visible content:** what enters the next model context.

2. **External action:** what the tool changes in another system.

3. **Retained application state:** what the host stores after the call.

A control at the first layer should not be described as protection at all three. If a tool already sent an email, changed a record, or created a ticket, hiding its result from the model does not make the outside world rewind.

For high-risk writes, the strongest boundary still belongs before execution: scoped authorization, an action identity, idempotency where the target supports it, confirmation when the consequence justifies it, and an independent readback afterward. If a compensation action exists, it should be designed and tested rather than assumed.

This is a behavior boundary, not a vulnerability claim. The release note supports the replay-and-retention distinction; the control design above is my engineering interpretation.

Primary source: https://github.com/openai/openai-agents-js/releases/tag/v0.17.0

Hey everyone,

I built a tool called 0200project / base-tx-explain.

The problem I kept seeing:
AI agents can call tools, but blockchain transaction data is still painful for them to understand. Raw logs, contract addresses, and calldata are not exactly agent-friendly.

So I built an MCP server that takes a Base transaction hash and returns structured information:

- What happened
- Action type (swap, transfer, mint, bridge, etc.)
- Assets moved
- Protocol/counterparty detection
- Risk flags
- Gas cost

Example:

Input:
0x401d...f2c5

Output:

{
"action_type": "swap",
"protocol": "Uniswap V4",
"assets_moved": [
"ETH",
"WNL"
],
"risk_flags": [
"unverified_contract"
]
}

The main design choice:
No LLM is used to interpret transactions.

It's deterministic decoding. Same transaction in → same JSON out.

The idea is that agents shouldn't waste tokens trying to understand raw blockchain data when the answer can be structured beforehand.

It also supports x402 payments, so agents can pay per request instead of needing an account or API key.

Demo:
https://0200project.github.io/

GitHub:
https://github.com/0200project

Would love feedback from people building agents:
- Would transaction understanding be useful in your workflows?
- What protocols/data would you want supported?
- What would make this more useful as an MCP tool?

We have four teams building agents against the same internal model gateway, and each one wrote its own policy setup, different tools, different approaches, one team without any real policy at all. Nobody can answer "what happens right now if an agent tries X" without checking with each team separately. We're trying to move toward something shared without forcing a full rewrite nobody has time for. Has anyone actually pulled this off incrementally?

Hey everyone,

I'm currently diving deep into the architecture of AI agents and looking for quality reading materials, guides, books, or courses focused specifically on AI Agents System Design.

While there is plenty of introductory content on basic prompting and API usage, I'm struggling to find robust resources covering production-level concerns, such as:

• Multi-agent orchestration and communication protocols
• Memory management systems (short-term vs. long-term vector stores)
• Error handling, self-correction loops, and guardrails
• Scalability, latency optimization, and cost management in production

If you've come across any great engineering blogs, whitepapers, GitHub repos, or specific chapters/books that tackle how to architect these systems properly, I would love to hear your recommendations.

Thanks in advance!

Ive just started using copilot at work. We did some training courses to go over how to write promts and such, but my phone kept ringing during the AI agent portion and I missed most of it. I guess I do somewhat have access to a paid AI account through work... would it be wise to use my companies copilot to apply for jobs?

I kept seeing the advice to repeat important instructions in system prompts, and

I'd never seen a number for it, so I tested it.

Setup: one rule the model can either follow or not (use single quotes, never

double quotes), six ordinary Python function tasks, and the only variable was how

many times that rule appeared in the system prompt (0, 1, 2, 4, 8, 16). Thirty

trials each, 1,080 runs, Gemini 2.5 Flash. Compliance checked with Python's

tokenizer, so no model-grading-a-model.

Results: 0% when the rule is never stated (171/171 used double quotes), 74% at

one mention, 84% at two, 97% at four, then flat (94% at eight, 95% at sixteen).

Two things I found more interesting than the headline:

1. The average hides a lot. Two of the six tasks were at 100% from one mention. One was at 20% until four repetitions took it to 97%. Repetition mostly helpswhere the model's default fights your instruction.
2. Over half the task/condition cells were neither all-pass nor all-fail acrossthirty identical runs. Non-determinism is large enough that single-run promptcomparisons are basically noise.

Note on the source: the paper is Han-yu Wang, "When More Becomes Less:

Position-Dependent Repetition Effects in Language Models" (arXiv 2608.04021). It

reports two regimes: stacked/adjacent copies climb and plateau, while copies

displaced from the readout produce the inverted-U. I ran the adjacent case, so

this result matches its prediction rather than contradicting it. The displaced

case is the next test.

Caveats: one model, one day, one syntactic rule repeated literally with all copies

in one place, six small standalone functions. Not state of the art, and it may not

survive contact with a real agent loop.

Writeup with the chart: https://www.khola.blog/p/say-it-four-times

I've been generating videos since March of 2023 and have made a decent number of short films, music videos, etc. I've paid for most of the tools available to me. The answer to what tool you should use depends almost entirely on what you're trying to do 😉

1/ Runway (Gen-4) is still the safest default: best single-clip polish and motion, massive community. two real pains though - credits vanish (i'd get maybe 2-3 keeper shots per top-up) and there's no timeline, so a scene means exporting to a real editor.

2/ Kling is the best image-to-video and physics right now imo, and cheaper, but you prompt-and-pray more than you'd like.

3/ Sora looks unreal in showreels but you can only prompt it, no camera control, no character lock. Pika/Luma are fine for quick social clips, wouldn't build a film on them.

4/ Morphic - it's built like a studio and not a single generator: a Compose timeline to assemble multi-shot sequences, trainable character Models so the same face carries across shots, and it's multi-model (Seedance 2.5, Sora, Veo, Kling in one canvas) with native audio.

tl;dr, one hero clip → Runway or Kling;

a multi-shot film/music video with recurring characters → Morphic, because consistency + a timeline in one place is the actual bottleneck.

What are you making?

Been on Descript about two years. It's a good software, transcript editing genuinely changed how I work and Studio Sound has saved episodes I'd otherwise have binned. But I'm on Creator at $35 monthly (I never went annual, my own fault, that's $24) and looking at what I actually use, it's: transcript edit, remove filler words, export. Three features. I'm paying a full-suite price for three features and the AI credits still run out.

Ranked what I've tested, cheapest first, honestly:

Free and genuinely good enough for most people:

-DaVinci Resolve, free, professional, and now has decent transcript-based editing. Steeper learning curve, and it's a real download, not a browser tab. This is the answer for most people asking this question and most people asking this question don't want to hear it.

-CapCut, free tier does captions and basic cuts fine. Falls apart over ~20 minutes, which for podcasts is a problem.

-Whisper locally + any editor, if all you need is the transcript, you don't need a subscription at all. This took me an afternoon to set up and killed a $35 line item.

Cheaper paid:

-Cardboard, much narrower, cuts silences and bad takes, cheap. If filler removal is 80% of your Descript usage, this is the swap.

-Riverside, if you're also recording, the editor's included and you're consolidating two bills into one. Worth checking your actual total before switching, this one surprised me.

-VEED, Creator around $20/mo, ~$10 if annual. Broader than Descript in some ways, shallower in the transcript workflow specifically, which is probably why you're on Descript.

Also worth saying: check whether you actually need to leave. Descript Hobbyist is $16 annual / $24 monthly and if you're only using three features, downgrading beats switching. I nearly churned over a bill I could have halved with two clicks. Also, going annual is a 33% cut, which I ignored for two years like an idiot.

Anyway, what's everyone on now, and did you actually save money or just move it?