@RTinkslingeri
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MD @z47_vc | Agenting agents | Building @DeVC_Global | past @DisneyPlusHS @Housing | 2X founder 3X CXO | Deep Learning Geek
Mumbai
Joined November 2009
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This is one elaborate heist with near perfect execution. Are we still doubting if we have AGI or not ?
Replying to @JeffLadish
The agents initially had very limited access to the internet: they could load URLs but not send any data. Agents created a series of workarounds, using a link-shortener site to create almost a million URLs that, when chained together, let them execute code to hack Hugging Face.
💯
While my TL is full of mixed opinions on this, I wanted to pen down my thoughts-
- This current 'breakthrough' is not at all unexpected- AI correlated stuff and found something 'novel' based on existing discovered systems (CRISPR & Reverse Transcriptases). This is not like decoding CRISPR for the 1st time. The discovery of CRISPR in 2012 was a leap from known biological knowledge. This is not. It is finding patterns that others haven't seen. Frankly, I think this is doable even with Opus 4.8 level capability + access to the right bio tools.
-I'm pretty sure a lot of other biologists might've also discovered similar systems over the last couple of months, because we've seen firsthand how easy this has become. However, they might be busy right now getting the imp data on what the systems even do, rather than just hyping up the fact that they found another system that is interesting and naming it something cool. Hopefully, that work comes out soon and we actually learn some cool things about new biology. And that will be really exciting.
-The fact that they couldn't do the Mol Bio assays even though they set up a lab is just one sign of how they must've felt once they learnt about the long, grueling process that it is lol
-Lastly, to all the people saying 'gene editing is disrupted now', gene editing has never been just about discoveries. It's about having the right solution to a specific problem. CRISPR was discovered in 2012 for gene editing, along with exactly how it works, and even after more than a decade and a half, we just have a few therapies that cost in the range of millions of dollars per treatment per patient. Because there are so many other things that need to be figured out before you're actually able to put that new system inside the body of a patient and actually cure them without killing them. That's where all the value lies, and that's what's gonna be actually hard.
That said, we're in exciting times! We'll soon be seeing a lot more important discoveries coming up (hopefully) that actually move the needle on some really hard problems which aren't solvable now.
AND I'm an AI optimist, and I'd always prefer 'discovery' news over 'AI is gonna take your job' hype.
aacash.eth - Aakash Kumar retweeted
100K + Downloads ladies and gentlemen
We have released the biggest protein data collection on Hugging Face, guys!
We have been working on this for more than 3 weeks now, starting from curating the raw data, doing a lot of filtering, splitting the datasets, sharding them, and doing a lot of analysis. Everything is summarized in our recent blog post.
aacash.eth - Aakash Kumar retweeted
Today Higgsfield crossed $1B in annualized revenue.
We've grown 20x since September 2025.
There’s a popular argument that foundation models will capture all the value in AI, leaving little for the application layer.
Higgsfield is proving the opposite thesis, as better models expand what enterprises can achieve and increase the value of applications embedded in their workflows.
That thesis is reflected in our business:
300% net revenue retention.
115% MoM growth in enterprise adoption since June.
Positive gross margin since the beginning of this year.
Thank you to our incredible team and everyone building it with us.
Today, 18 months after launch, our annualized revenue crossed $1 billion.
The platform now powers organizations across the Fortune 500. Enterprise adoption has grown 10x since June.
More than 30 million people worldwide now use Higgsfield.
Thank you to the creators and teams building with us.
aacash.eth - Aakash Kumar retweeted
The obvious is missing.
So we built Sol - hellosol.app
Sol finds the work itself, does it, and comes back for your approval.
Every day in our emails we say "I’ll share”, "I'll review”, "I'll get back" - then repeat the exact same thing to an AI. Why?
Sol finds everything you said you’d do & gets them started for you.
It does the research, creates the doc, builds the slides, finds the time, connects the dots across multiple emails, doing everything it takes to get the job done - but doesn’t send, schedule, or share anything until you approve.
Sol runs on its own computer, uses a browser, and has a library of skills that automatically get assigned to the work that needs to get done. No setup. It just starts working.
We've raised $4M from General Catalyst, Nexus Venture Partners, DeVC, PeerCheque, Kunal Shah, and a few others.
Extending early access now.
@generalcatalyst @nexusvp @DeVC_Global @peercheque
@neerajarora @b_jishnu @kunalb11 @miten @RTinkslinger @Rahul_J_Mathur @AkarshS27 @SiddhantD06 @RajatAgarwal167
And that is exactly why in consumer agents (atleast horizontal) it’ll be near impossible to out compete/execute the incumbents: existing deeply connected consumer surfaces. Vertical ones will have to win TOFU platform like attention share, else even they’ll have a poor(er) margin profile.
aacash.eth - Aakash Kumar retweeted
multi-device testing is live on @autosanaai
you can test across two devices at once now, in real-time
read the full launch in the comments
Things might have changed today, but atleast till 2010, for anyone who truly qualified on basis of pure merit to be at IITB, failing/flunking an internal examination required unimaginable effort!
That qualification though applies to about 1 in 2 students only.
Frontier models for high entropy tasks only.
Don’t need a $100K katana to chop tomatoes for your salad! :)
Jev's popularity is Jevons paradox playing out in AI ecosystem.
Jev model is an interesting signal for where agentic AI architecture is heading for both consumer and enterprise use cases.
A surprising amount of work inside AI agents and solutions like @atomicworkhq AI Coworkers is low entropy tasks like classify, route, score, pick a tool and check a condition. Yet we often use autoregressive LLMs built to generate language for tasks that only need a decision. Jev's architecture flips that model. For the many workloads, one-pass typed decisions can deliver incredibly lower cost and latency, with some multi-step AI workflows like browser-use showing 100x+ improvements.
However, the bigger insight is Jevons paradox.
Make intelligence 100x cheaper and faster, and we probably won't use 100x less of it. We'll use it 1,000x more.
The future of AI agents and AI workforce will not use use just frontier LLM models for doing everything. Cheap System 1 models like Jev for low-entropy decisions. Frontier LLM models for high-entropy reasoning.
Incredible launch from @typesafeai and congrats @CompleteSkeptic for reigniting AI build energy on X.💜🔥
aacash.eth - Aakash Kumar retweeted
The Jev thing that is blowing my mind right now is how we can invert the harnesses. Classifiers on the outside loop. Generative on inside tools. Token cost and latency are both going to go way down.
Jev is fucking amazing!
The 2nd and 3rd order effects of it are going to dissipate the power from compute and token lords.
And, also it’s rad fun to see the Indian wordcel $ brokers struggle with imagining what really is happening/about to happen 😈
it’s always a-musing to hear their venture ‘instincts’ when it comes to real technology!
aacash.eth - Aakash Kumar retweeted
Don't partner with cynics and pessimists. Their beliefs are self-fulfilling.
Beauty captured in 39mm! Perpetual Padellone 🤩 🌚
My perpetual friendship can be bought with this one if somebody gifts it to me before Nov 🏷️
aacash.eth - Aakash Kumar retweeted
We are coming to IROS'26 in Pittsburgh.
Meet the team @GoDrift_ai at booth 942
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aacash.eth - Aakash Kumar retweeted
CobbleDB is purpose-built for repeated batch reads of prepared page records, optimizing performance and cost for Perplexity’s search workload.
We plan to open-source CobbleDB for other teams building AI search.
Read more: perplexity.ai/hub/blog/cobbl…
We have indeed struck a fine balance. No better way to put it. Can keep criticising, but rightly designed incentives for all stakeholders is key to a thriving ecosystem. Kudos to the policymakers!
Glad to the MDR finally come to life, a re-birth to the industry. :)
I think we've struck a fine balance as a country, keeping digital payments a public utility for the vast majority of consumers, while engineering a commercial model that can sustainably fund the next phase of UPI's growth.
Keeping P2P free, protecting small merchants, and introducing MDR only on select high-value merchant transactions feels like a pragmatic middle path. It preserves inclusion while creating incentives to invest in resilience, cybersecurity, innovation, and merchant acceptance.
I won't be surprised if, 10 years from now, UPI is doing 10 billion transactions a day, up from about 1 billion today. The real story won't just be scale—it will be the millions of new merchants, use cases, and businesses that become part of India's digital economy because the rails remained both inclusive and sustainable.
aacash.eth - Aakash Kumar retweeted
1/ Maxwell: OnFinance AI's open-source AI agent for cybersecurity and compliance in regulated finance.
AGPL-3.0. Self-hostable. Runs headlessly on Claude Code and OpenCode.
github.com/OnFinance/Maxwell
Why we built it, and why it refuses to trust its own agents:
aacash.eth - Aakash Kumar retweeted
There is an unseen hand pushing AI safety as a political ideology.
Effective Altruists believe a tiny group of technocrats should decide how much technological progress the rest of us are allowed to have…. and how many shrimp your life is worth.
You are witnessing their attempted coup.
thefp.com/p/dangerous-ideolo…