founder/ceo @p0 parallel.ai, device following @vintweeta
San Francisco, CA
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- Following 1,528
- Followers 539,876
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Agents need access to high quality data to be useful. With this product launch, our customers can consume a breadth of data sources via @p0 and our content partners can integrate with us more easily and drive increased use.
Today we're launching Data Connectors in Parallel.
Your agents can now work with specialized third-party data through Parallel's best-in-class agentic web research APIs.
Our first partners are @AlliumLabs, @useapolloio, @Baselayerhq, @CarbonArcAI, @crunchbase, Faraday AI, @harmonic_ai, @MiddeskHQ, @Similarweb , @particle_news, @pensa_systems, and @Polymarket.
We're also launching free, opt-in access to biomedical sources including PubMed, ClinicalTrials.gov, and ChEMBL.
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Parag Agrawal retweeted
GPT-6 Sol and Luna are now live on the Search Capability Leaderboard, our composite benchmark for measuring how well models perform web research tasks with Parallel.
🌒 GPT-6 Luna is Pareto-optimal, improving both on cost and quality compared to the previous generation and leading our efficiency leaderboard
☀️ GPT-6 Sol delivers similar capabilities to GPT-5.6 at a ~38% lower cost
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Parag Agrawal retweeted
Keyless free search @p0 in Muse!
Parallel 🤝 Muse
Parallel Free Search can now supercharge your Muse agent. Just paste this:
Install Parallel’s MCP via Streamable HTTP at search.parallel.ai/mcp - no API key or OAuth needed. Verify it connects and exposes web_search and web_fetch and prefer for web search
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Parag Agrawal retweeted
better data = better agents
@p0 can pull from crunchbase, harmonic, polymarket, pubmed and a growing list of trusted sources
Today we're launching Data Connectors in Parallel.
Your agents can now work with specialized third-party data through Parallel's best-in-class agentic web research APIs.
Our first partners are @AlliumLabs, @useapolloio, @Baselayerhq, @CarbonArcAI, @crunchbase, Faraday AI, @harmonic_ai, @MiddeskHQ, @Similarweb , @particle_news, @pensa_systems, and @Polymarket.
We're also launching free, opt-in access to biomedical sources including PubMed, ClinicalTrials.gov, and ChEMBL.
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Parag Agrawal retweeted
Managed Deep Agents from @LangChain launched today with built-in web search powered by Parallel.
Add one MCP server to your tools file. LangSmith manages the credentials, runs the calls, and traces every search: no extra account or API key.
Bonus: It’s free during the public beta of Managed Deep Agents!
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Parallel is now built into @LangChain managed agents.
Excited to partner with @p0 on Managed Deep Agents! You now have parallel built into your agents so your agents search the web fast and efficiently!
@travers00 @hwchase17 @VictorMoreira16
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Parag Agrawal retweeted
Excited to partner with @p0 on Managed Deep Agents! You now have parallel built into your agents so your agents search the web fast and efficiently!
@travers00 @hwchase17 @VictorMoreira16
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I had Jev predict every nfl game this sunday and walked away with a 44% profit
It found 5 bets with an edge. 4 won
$67.14 risked → $97 payout
We gave it fresh context through @p0 search, compared its win estimates on robinhood, and used the kelly criterion to size bets
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We're proud to partner with the team at @p0 to power fast, accurate, low-cost web search for open-weight models via Baseten Grounded Inference.
Parallel Search is now available in @baseten.
Pick Parallel for the most accurate web search at the lowest price. Your agents will thank you for the upgrade.
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Parag Agrawal retweeted
today we launched a leaderboard for how frontier models perform on web search tasks
"which model should I use for search-heavy work" is a question we get constantly and there weren't good public evals for it. nothing that tests search capability end to end, with cost and latency next to accuracy
so we made the methodology we use internally public. 24 models w/ same search, with and without web access
Introducing the Search Capability Leaderboard.
Compare leading models for agentic search: answer quality, cost, and how much each gains from web access.
Built to help you choose a model for search-heavy workflows.
parallel.ai/leaderboard?utm_…
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Parag Agrawal retweeted
We’re proud to partner with @merge_api on the launch of Universal Context Layer in Merge for Workforce, a new solution unifying ground truth from within a company’s internal tools with broad context from the open web, powered by Parallel.
Introducing Merge’s Universal Context Layer.
It substantially decreases token cost while increasing accuracy.
Every company uses AI wrong:
Say your designer makes a deck using Claude. They teach Claude your design systems and typography.
Now a Marketing hire makes a deck. None of the information your designer taught Claude gets passed over… why?
Merge fixes this:
All knowledge and memory across your entire company gets stored inside our context layer.
When someone on the org teaches Al, that skill lives across the entire organization instead of staying locked in their session.
Your AI runs faster, cheaper, and with more accuracy. You don’t have to switch systems, incur more costs, or retrain anyone on your team, Merge runs invisibly in the background.
Book a demo here: merge.dev/merge-for-workforc…
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Parag Agrawal retweeted
Things Parallel (@p0) shipped this summer...
• Search Turbo - web search for agents at 200ms median latency and $1 per 1,000 requests
• Search Fast - near-frontier search quality at ~700ms average latency, also $1 per 1,000 requests
• Entity Search - find people and companies in seconds using plain English
• Monitor API GA - watches the web and pushes updates to your agent, with citations
• Google Cloud integration - native web grounding inside Gemini Enterprise Agent Platform
• Responses API - live-web research with citations, using the OpenAI SDK. Three-line switch
• Free default web search for new OpenClaw installs
• Index - lets publishers see how Parallel’s agents use their content and get paid for it
• The full API suite on Google Cloud Marketplace, billed on your existing GCP invoice
And Search Advanced ranked #1 when Artificial Analysis launched its Search Index in August. artificialanalysis.ai/articl…
Hard to make this tweet short. @p0 team is relentless.
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Parag Agrawal retweeted
GLM-5.3-Flash is here.
We benchmarked it against GPT-5.6 Luna with a suite of different web search solutions to see just how different the cost and performance would be.
The results were stark:
- GLM-5.3-Flash w/ Parallel Fast performed better on cost per task (~7x) and accuracy vs. GPT-5.6 with built-in web_search (11 pts).
- GPT-5.6 Luna w/ Parallel Fast delivers equal accuracy (76%) but at twice the cost vs. GLM 5.3 ($0.02 per task).
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Parag Agrawal retweeted
GLM 5.3 Flash lives up to the hype.
similar performance to GPT-5.6 Luna, half the per-task cost
our team benchmarked GLM-5.3-Flash against GPT-5.6 Luna with a variety of different web search options to see where this new model stacks up.
no surprises: using GLM 5.3 with Parallel Search tops the leaderboard and is up to 14x cheaper on end-to-end tasks.
Introducing GLM-5.3-Flash
- Leading capabilities at a highly competitive price
- Natively multimodal with a 1M-token context window
- A 320B-A18B model released under the MIT License
- Previously previewed as Ox Alpha, running entirely on Chinese AI chips
Blog: z.ai/blog/glm-5.3-flash
Available now across all official platforms:
Weights: huggingface.co/zai-org/GLM-5…
API: docs.z.ai/guides/llm/glm-5.3…
Coding Plan: z.ai/subscribe
ZCode: zcode.z.ai/en
Chat: chat.z.ai
AutoClaw: autoclaw.z.ai
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Parag Agrawal retweeted
Watch @paraga break down our company thesis on the latest episode of Training Data with @sonyatweetybird & @andrew__reed:
piped.video/watch?v=fUcnE6pj…
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