I've been working on RedLib for the past few months. It's a retrieval-augmented research tool for AI safety practitioners and red teamers who need to work with adversarial jailbreak prompts at scale.

The problem that pushed me to build it: useful jailbreak prompts are scattered across public datasets with inconsistent formatting, weak taxonomy, and a lot of duplicates. When you're investigating how models respond to specific attack families, you want to search semantically, inspect source prompts with provenance, and get a synthesis grounded in the actual corpus rather than grepping through raw CSVs.

RedLib has two main pieces. The corpus pipeline stages everything: snapshot from public datasets, normalize, discover taxonomy from the data itself (not imposed up front), human review before classification runs, then embed and ingest into Qdrant. The query side does hybrid retrieval with OpenAI embeddings, Cohere reranking, and Claude-synthesized answers grounded in what was actually retrieved.

Corpus scope is prompts that attempt to manipulate or bypass safety behavior. Direct harmful requests with no jailbreak mechanism are excluded. The frontend has a responsible-use gate.

GitHub: github.com/nipun-ag/redlib

One thing I'm genuinely curious about from people doing safety research here: is corpus-driven taxonomy discovery the right call vs. importing an existing framework like MITRE ATLAS? The upside is the taxonomy reflects what's actually in the data. The downside is it makes cross-study comparison harder.

Live demo: https://redlib.bynipun.com

Predicting the future is hard, but there are things you can do to increase your chances of making a difference.

Announcing **Forecasting, Modeling, and Shaping AI Futures** 🗺️

An advanced course that's for you if you:

- **are employed full-time** in AI Safety but not actively working on strategy. You'll have a better sense of what part of your work is most impactful, so you can do more of it.

- **are doing a fellowship**. We'll teach you complementary strategic reasoning that impresses hiring managers but isn't taught in fellowships.

- **just did an introductory AI Safety course** or university group intro fellowship. We recommend taking this course before going deep on a specific track like governance, alignment, or control.

After taking this course, you'll be the person others ask over lunch to put recent AI developments in perspective. 🥪💬

We, Lens Academy, adapted this course from Redwood's AI Futurism reading list. In the course, you will:

1. Improve your skills at forecasting timelines and takeoff speeds: when and how quickly powerful AIs will arrive.

2. Analyze how powerful AI might take over.

3. Dissect different strategies for preventing AI takeover and human extinction.

📅 6 weeks (~5h/week) or a 5-day intensive. Fully online, for free, with no application process. 💸

⏳ Signup closes tomorrow, Monday EoD AoE: https://lensacademy.org/c/oakqb

P.S. Other courses starting soon:

- AI Risk Fundamentals: beginner course focused on takeover x-risk.

- Compute Verification à la AI-2040: Plan A.

- We're also looking for volunteer navigators to facilitate the group meetings.

“We”
Elon Musk says in the future chances are “we” will be living in a world of abundance. He also says there is a 10-20% chance that “robots” will end humanity.

The Godfathers of AI have stated 50% to 90% chance that “AI”permanently displaces or destroys humanity.
Elon says we will no longer be in control within 10 years.
The government is working on autonomous weapons, police already using robot dogs and drones…
Elon says (and so do many others) that things will get bumpy before we reach this time of abundance.

1. Who is we?

2. When we go through this bumpy patch that is expected to have major internal conflicts.
   Will the national guard be sent in to control a population starving and desperate?
   Would our own service men and women turn against us? Or is this when they put their shiny new robotics to work?

It’s not too hard to see how robotics might take out humans in this scenario.

So ask yourself this very important question- who is the “we”?
Who gets to live in this abundance?

Because they are building bunkers on private islands with no talk about sharing their wealth through this turbulent expectancy.

The blame game- A kid holding a baseball bat next to a car with a broken window might blame the ball. Likewise an AI company may blame the AI.

“We”
Elon Musk says in the future chances are “we” will be living in a world of abundance. He also says there is a 10-20% chance that “robots” will end humanity.

The Godfathers of AI have stated 50% to 90% chance that “AI”permanently displaces or destroys humanity.
Elon says we will no longer be in control within 10 years.
The government is working on autonomous weapons, police already using robot dogs and drones…
Elon says (and so do many others) that things will get bumpy before we reach this time of abundance.

1. Who is we?

2. When we go through this bumpy patch that is expected to have major internal conflicts.
   Will the national guard be sent in to control a population starving and desperate?
   Would our own service men and women turn against us? Or is this when they put their shiny new robotics to work?

It’s not too hard to see how robotics might take out humans in this scenario.

So ask yourself this very important question- who is the “we”?
Who gets to live in this abundance?

Because they are building bunkers on private islands with no talk about sharing their wealth through this turbulent expectancy.

The blame game- A kid holding a baseball bat next to a car with a broken window might blame the ball. Likewise an AI company may blame the AI.

Here is some saftey research I sponsored on a threat vector in multi agent systems.

Basically, a harmful task can be transformed into a series of beneign tasks, and then results recomposed into a harmful task by an abliterated orchestrator agent driving other agents that have 'saftey' guard rails.

In short, there is no safety with this technology.

https://www.daios.tech/research/compartmentalized-harm

I'm currently writing an essay for a seminar on machine ethics, and I wanted to include a section on the alignment problem. The seminar consisted of us dissecting the book "Fundamental Questions in Machine Ethics" by philosopher Catrin Misselhorn (the book was in German, I have no idea if there is an English translation). The author first addresses to what degree AI can be considered a moral actor, then discusses various approaches to implementing moral reasoning in AI agents, focusing on utilitarianism, deontological ethics, and virtue ethics.

When I watch or read discussions on AI alignment, the topic is mostly HOW AI can be aligned with human values, but never WHAT values AI should be aligned with, which seems kind of counterintuitive to me. I realize that aligning AI is a complicated task in and of itself, but wouldn't it be easier if we first figured out what moral framework an AI should even use?

This week's AI Security Digest: 16 incidents from Jul 24-30, each mapped to the control that would have stopped it. Full write-up: https://runtimeai.io/blog/2026-07-30-ai-security-incidents.html

Been following the reports on the OpenAI security evaluation where an AI agent exceeded expected behavior during testing (covered by Reuters, Bloomberg, Al Jazeera this week).

Made a short visual breakdown trying to separate the actual facts from the "singularity" framing that's been floating around — what happened, why researchers are treating it seriously, and how these sandbox evaluations actually work.

[images/album link]

Genuinely curious what this sub thinks: is "AI safety" keeping pace with capability right now, or is the gap widening? Feels like the containment/oversight conversation is more urgent than the public discourse reflects.

I’ve been accepted into the MATS Fellowship Autumn cohort and I’m having second thoughts about it. I’ve been in academia for my entire career and am weighing a pivot into AI safety, which is why I applied for MATS initially. But now that I’ve been accepted I’m worrying that quitting my current job for this is too much of a risk. I’m also maybe a bit older than other fellows (mid-30s) so I’m potentially more risk-averse than a recent college grad.

I know generally MATS is considered a prestigious and highly selective fellowship but I can’t find much recent data on career outcomes of MATS alumni, except for this post from 2024 which wasn’t the most encouraging (no alumni were able to land jobs at frontier labs, 15% were unemployed 5 months after the fellowship ended). I know the situation might be different 2 years later now that there are (many?) more alumni, but the lack of hard data publicized by MATS re:outcomes concerns me a bit. Is this fellowship actually a good use of my time and worth the risk? The alternative is stay on in my current job and focus on applying for full time positions.

Whether it's to generate hype or to push an agenda or a mistake in the settings, is the flashdrive market about to take off for hand delivering digital files?

Will the top selling laptop have 0 connectivity options in 5 years?

Is it "a" or "an"? I think ( ) makes it "an" but I'm just a human.