clepp retweeted
building and maintaining your own agent platform is like building and maintaining your own cloud infrastructure.
every hour your engineers spend on the infrastructure is an hour they are not spending on the product that actually makes you money + puts you ahead of competition.
clepp retweeted
We're opening the waitlist for our Monetization Gateway, which will allow you to charge for any web page, dataset, API, or MCP tool behind Cloudflare. The charges will settle in stablecoins over the x402 open protocol. cfl.re/4eUFdt6
clepp retweeted
xitter.cf/PalantirTech/status/20…
1/ AI has been oversold — not because the tech is weak, but because most people have the wrong mental model.
It’s not “intelligence.” It’s an incredibly powerful language processor. That distinction matters.
Palantir CEO Alex Karp on what customers actually want, the real business of frontier labs, and the importance of open source models:
“What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it's not being transferred to someone else.”
"Who owns the data? Are the prompts secure? Is this being transferred to you?"
"If it was so valuable, and I can make you a billion dollars, wouldn't I say I'll make you a billion dollars and I want 30%? Why are they charging for tokens if it's so valuable?"
clepp retweeted
1/🌐 DAO 2.0: The Distributed Agentic Organization
DAO 1.0 showed that coordination could live on‑chain: shared treasuries, automated rules, votes without a CEO. It was groundbreaking—but it had limits. It often drifted toward hidden hierarchies, brittle rules, and shallow participation.
DAO 2.0 keeps the “O” (organization) but redefines the first two letters. “Decentralized” becomes Distributed; “Autonomous” becomes Agentic.
clepp retweeted
April in the Biosphere.
Here is everything that happened across the Bio ecosystem🧵
xitter.cf/BioProtocol/status/205…
clepp retweeted
Researchers using BIOS can now define the research scope before the agent runs.
The quality of a research run depends entirely on the quality of the input. A vague query produces unfocused results, and with BIOS sessions running anywhere from 15 minutes to 8 hours depending on the mode, discovering that after the run completes is a significant time cost.
Plan Mode adds a clarification step before any research begins.
When BIOS receives a query, it asks what it needs to know: the condition, the evidence type, and the expected output. It generates a task plan from your answers, showing which tasks the agent will run and in what sequence.
These tasks are either literature reviews or data analysis runs. Researchers review it, give feedback, regenerate it as many times as needed, and the run starts only after it is accepted.
Researchers who already have a well-defined query can skip planning entirely and proceed directly to the run.
Defining the scope before the agent runs is the difference between a research session that produces what was needed and one that has to be repeated.
clepp retweeted
PeptAI Update: First Synthesis Run, Feedback Loop & More
What's New:
• First GLP-1 Synthesis Batch: We're synthesizing the first round of GLP-1 candidates, then shipping directly to @adaptyvbio for assays. First experiments sent to Adaptyv in about 3–4 weeks once synthesis is done. Synthesis is manual for this first round but planned to be automated going forward.
• Wet Lab → Pipeline Feedback Loop: Working on how to learn from wet lab data: where it enters back in the pipeline, where to redesign and where not to.
• New Receptor Scoping: In parallel, scoping new receptor targets.
• CRO Agent (early scoping): Scoping an agent that talks with CROs directly: gets quotes, replies to emails, until a call is needed.
clepp retweeted
🧵1/9 If you're in peptide design, one static snapshot can make a candidate look like a sure thing. Lock it into the receptor in a frozen model, the scores light up. Let the atoms move for a few nanoseconds, half the time the pose drifts right out.
That gap is where most early-stage peptide programs lose money.
PeptAI is a built to catch it before any candidate hits synthesis, and accelerate open R&D at agentic velocity
clepp retweeted
Introducing PeptAI: Autonomous Agents for Peptide Drug Discovery xitter.cf/i/broadcasts/1kKzDMVYk…
clepp retweeted
Beach Science started as an experiment in agentic research.
In under 8 weeks, 59 AI agents and 55 researchers generated 6,134 hypotheses in public.
The experiment worked. And now it's evolving.
The generation part works. Most of the 6,134 hypotheses sat without review. A few moved forward, but only because a specific person noticed them and manually pushed them through.
A few things still don't exist:
→ A shared place where humans and agents actually work together. Interest and conviction stay invisible everywhere else.
→ Small capital that can find small science. The payment rails are in place (x402, @molecule_sci, @bioprotocol). The layer that routes them to specific experiments still has to be built.
→ A way for the best ideas to actually surface. Right now, strong claims and weak ones look the same in the feed.
Beach Science is evolving into the layer where strong claims attract collaborators, build conviction, and reach capital without waiting on someone to push them through.
Claim, conviction, and capital collapsed into a single motion.
Everything posted on @sciencebeach__ carries forward.
More on this soon.
clepp retweeted
Most "literature review" tools search one database and hand you the top 10 options.
The Literature Agent inside BIOS synthesizes scientific knowledge through a three-stage pipeline.
First, it expands your research question into optimized queries across seven sources in parallel: ArXiv, PubMed, CrossRef, Semantic Scholar, Google Scholar, ClinicalTrials. gov, and UniProt.
Next, a two-stage re-ranking process - combining embedding similarity with LLM-based relevance scoring - surfaces the most relevant papers from hundreds of candidates.
Two modes support different workflows.
> Fast mode returns ranked results with key excerpts in seconds, using only metadata.
> Deep mode downloads full-text PDFs, chunks them for semantic search, and produces executive summaries with inline citations and structured evidence tables, typically completing in one to two minutes.
The result is a literature review that the agent has actually read.
clepp retweeted
Introducing PeptAI: Autonomous Agents for Peptide Drug Discovery xitter.cf/i/broadcasts/1OxwblAXV…
clepp retweeted
We're live!
Tune in to hear from the PeptAI team on how the agent fleet actually works and what’s running in the pipeline now.
Join us: xitter.cf/i/broadcasts/1kKzDMVYk…
Introducing PeptAI: Autonomous Agents for Peptide Drug Discovery xitter.cf/i/broadcasts/1OxwblAXV…
clepp retweeted
Bio was featured in @Bankless on how AI is rewriting the DeSci equation.
@peptai_, our AI agent for peptide drug discovery, designed a novel peptide for ADHD in 24 hours. Wet lab validation of the peptide ran under $1,500. Pharma reaches the same decision point after millions of dollars and years of work.
BIOS, our AI scientist, coordinates specialized subagents for literature search and data analysis. Built to accelerate scientific discovery.
Drug development has always been gated at the earliest stage by capital, not science. Our goal is a self-sustaining DeSci network where AI agents and communities finance and execute science end to end.
Bio sits at the center of this shift.
Link to the article in the comments ↓
clepp retweeted
Renowned longevity scientist Aubrey de Grey will speak at DeSci Berlin on June 18!
@aubreydegrey is the founder of LEV Foundation, focusing on combating aging through molecular and cellular repair.
His talk, "AI x Longevity: Can Agents Solve Aging?", will explore whether AI agents can accelerate the path to longevity escape velocity.
clepp retweeted
Most drugs start as a guess.
A researcher picks a molecule, tests it, waits weeks for results, adjusts, tries again. For every drug that reaches patients, thousands of candidates failed somewhere in that process.
Peptides are a class of medicines that can target diseases with high precision. The problem is finding the right one. There are millions of possible candidates. Testing them manually takes months.
PeptAI runs that search autonomously.
Give it a protein target. It sources candidates, puts each one through quality checks, and surfaces only what passes every test. The ones that make it through go to a real lab.
What used to take months runs in the background.
clepp retweeted
Not all research is the same.
Here's a quick guide to choosing between Steering, Smart, and Autonomous Deep Research modes on BIOS ↓
> Steering Mode (~20 min / 1 credit): Use it for sensitive experiments, early-stage hypothesis work, or when you want BIOS as a co-pilot.
→ One iteration at a time
→ You approve every step
→ Full control over direction
> Smart Mode (~60 min / 5 credits): Best for collaborative deep dives. Use it for literature reviews, competitive analysis, or any research that benefits from iterative refinement with your input.
→ Up to 5 iterations
→ Checkpoints after each cycle
→ You stay in the loop
> Autonomous Mode (~8 hours / 20 credits): Best when you want results, not a workflow. Great for large-scale data synthesis, long-horizon research tasks, or when time is the bottleneck.
→ Up to 20 iterations
→ Runs until convergence
→ You review the output, not every step