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considering agents are going to be the primary users of defi in the next few years
good security researchers who understand how to hack harnesses, mcps, cloud infra AND smart contracts will be the next multi millionaires in web3 sec
we've been collecting some of the best papers on LLM routing over the past few weeks and throwing them in this repo as a research method
if you want to learn how to get your token cost down while maintaining the same quality from your agents or coding harness, this is for you
github.com/33Audits/llm-rout…
learning how to build secure, access-controlled, and governed agentic systems is going to be extremely important over the next decade
if you're in cybersecurity learning about AI is crucial, its no longer a choice
SCOOP: Major U.S. banks are detecting unauthorized third-party AI agents inside customer accounts, including some attempting to take actions that appear to be independent of user instructions, sources told Axios.
The findings have alarmed banking executives, who are racing to restrict access to their systems as AI moves at a velocity faster than financial institutions can respond, multiple industry and bank executives told Axios.
Several banks are now preparing for worst-case and even “day after” scenarios, including how to rapidly secure or restore customer accounts if things go wrong, the people said.
Several bank executives told Axios they're struggling to distinguish between human users and AI agents. One executive said banks also struggle to determine whether agents are authorized by customers or potentially operating without their permission, more akin to rogue agent behavior.
Read my latest for Axios here: axios.com/2026/10/22/ai-agen…
world-class guide on how to lead AI transformations at organizations
xitter.cf/vasuman/status/2112287…
happy to have collaborated with asyncswap early in their development process; this was purely an AI audit, and the first one we've done that way
results were great and we're looking forward to continuing our work with the team
lots more to come
happy to have collaborated with asyncswap early in their development process; this was purely an AI audit, and the first one we've done that way
results were great and we're looking forward to continuing our work with the team
lots more to come
new form of security, bet on which web3 protocol gets hacked next
the opportunity for service businesses in AI is still so untapped imo
being able to deploy AI agents, build MCPs, create workflows, and lead organizations through an AI transformation, with security as a priority, is a niche we've been diving into a lot lately
about half of our current clients deal with sensitive data and need private, governed, and secure AI implementations to be deployed for them
we've been working hard at finding creative ways to handle PII data for clients who deal with a lot of this sensitive data, and that alone has unlocked a huge niche for us inside of security/FDE
people are well aware agents are here, but many of them don't feel safe relying on Meta (a corporation that has historically been crap at handling user data) as a solution for their business
its insane how many different niches there still are with little competition in the AI services game
AI will change every industry as we know it. if you're already an expert in something, you can get really good at implementing AI and carve a niche for yourself with both
I remember shouting this from the rooftops a year ago when everyone was focused on making more SaaS:
AI is a services game. You cannot solve for enterprise inefficiency with tool adoption. You need AI implementers who go in, understand business processes, refactor for an AI-native reality, and then implement custom agents for those requirements.
Varick has been building in this domain for longer than anyone. Our client satisfaction and expansion speaks for itself.
Meta is just the latest to have entered the arena. I'm glad, because there are a million companies in the US alone that need a service like this, and Varick could never help all of them alone.
For companies that don't want to be vendor locked into any one AI company's systems, subjecting themselves to
- price hikes
- model quantization/regressions
- rate limits
- migration costs down the line
Let's chat.
We're also HIRING:
- FDEs
- Platform Engineers
If you're top 1% in your field, I want you at the #1 AI Transformation company in the world.
Lee | 33Labs retweeted
solid map for anyone looking to learn AI engineering
deeplearning.ai/resources/ai…
solid map for anyone looking to learn AI engineering
deeplearning.ai/resources/ai…
another glimpse at the future of web3 security: OpenAI is definitely watching what the best in web3 security are currently doing and planning their next acquisition based on that
if they're running tests like this internally, then they are for sure doing deeper work with smart contracts and chain-level security that they can't fully disclosed
cybersecurity in the agentic area will be a MASSIVE opportunity
position yourself accordingly
so you built a token that represents full range liquidity of a uni v4 pair
essentially giving users access to consistent impermanent loss completely removing the whole point of concentrated liquidity?
this industry is incredible
This weekend I invented something new, a new type of token using Uniswap v4 hooks that I call a Pair Token.
A Pair Token is a new type of ERC-20. It is a fused, auto-compounding asset, a fusion of two tokens.
For example, the stock tokens NVDA and SPCX fused into a single tokenized version of the two, with the ticker NVDASPCX, or ETH with NVDA, creating ETHNVDA.
Each Pair Token represents a full-range Uniswap v4 liquidity pool.
So why mint these tokens?
These tokens are self-multiplying in terms of value, as at any moment anyone can call the compound function to compound the LP fees generated by the pair’s trading activity back into the asset.
The token represents a new financial primitive, as it represents a proportional value and moves as an LP position NFT would, except that it is auto-compounding and, thanks to its v4 hook, allows for compounding and self-growth.
Since it is an ERC-20, this token can be freely traded onchain, LPed, or even paired with a launchpad.
A new type of token for a new type of exposure and strategy.
Starting tonight, you’ll be able to mint or redeem Pair Tokens for any pair on Robinhood Chain via the StonkBrokers website.
Minting and redeeming are free, and the compounding process on the tokens incurs a 10% fee on the LP fees generated, which is used for $STONKBROKER buybacks.
I look forward to a new line of products and protocols on Robinhood Chain using this new financial primitive.
Only on Robinhood Chain, powered by Uniswap. @Hookrfun
Lee | 33Labs retweeted
there will be hundreds of thousands of fine-tuned models for specific use cases and tasks.
the moat here is the proper data to train all these models
however has the data will have a ton of leverage. this is why so many of the new agent products are free or the price is subsidized
We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case).
I think we'll see more of this pattern inside companies. The frontier models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves.
We're making it easy to fine tune models internally for our employees. We even have a skill for it in our internal harness that makes it easy to spin these up and start training them:
"/fine-tune-and-serve-llm I want to fine-tune Qwen for payments risk review. help me get started"
I imagine we'll have some backend services that could be replaced entirely by this new architecture.
Lee | 33Labs retweeted
Anthropic dropped their own guide for how your SDLC should look if your team uses AI to ship code
- deterministic checks stay in CI
- model scans find contextual vulnerabilities
- recurring scans replace point in time reviews
- humans approve critical code
- take it one step further and add a custom security agent to this scanning the code in CI after every PR merge
there will be hundreds of thousands of fine-tuned models for specific use cases and tasks.
the moat here is the proper data to train all these models
however has the data will have a ton of leverage. this is why so many of the new agent products are free or the price is subsidized
We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case).
I think we'll see more of this pattern inside companies. The frontier models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves.
We're making it easy to fine tune models internally for our employees. We even have a skill for it in our internal harness that makes it easy to spin these up and start training them:
"/fine-tune-and-serve-llm I want to fine-tune Qwen for payments risk review. help me get started"
I imagine we'll have some backend services that could be replaced entirely by this new architecture.
Anthropic dropped their own guide for how your SDLC should look if your team uses AI to ship code
- deterministic checks stay in CI
- model scans find contextual vulnerabilities
- recurring scans replace point in time reviews
- humans approve critical code
- take it one step further and add a custom security agent to this scanning the code in CI after every PR merge














