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today, we're launching Light Parse Extend can now parse documents up to 70% cheaper than other solutions - light parse starts at $0.00625 per page, and scales down significantly with volume discounts - it's SOTA on Databricks OfficeQA Pro (a 3rd party agent benchmark) among all non-agentic parsing providers... show more

Kushal Byatnal

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49,774 görüntüleme • 1 ay önce •via X (Twitter)

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Kushal Byatnal profil fotoğrafı

Kushal Byatnal1 ay önce

light parse outperforms and outprices alternatives on document parsing Databricks OfficeQA Pro is a benchmark that measures how accurately an LLM can answer questions from scanned U.S. government financial documents this benchmark is truly independent. we ran the evaluation against it, but we had no part in designing it (nor did we commission it)

Ammaar Reshi profil fotoğrafı

Ammaar Reshi1 ay önce

whenever I see kb in a video, I’m airplaying it to my TV

Kushal Byatnal profil fotoğrafı

Kushal Byatnal1 ay önce

filming this video is what convinced me to go get a haircut

Ammaar Reshi profil fotoğrafı

Ammaar Reshi1 ay önce

I don’t see the difference

Davis Treybig profil fotoğrafı

Davis Treybig1 ay önce

Extend defining the full Pareto frontier for document processing!

Kushal Byatnal profil fotoğrafı

Kushal Byatnal1 ay önce

🫡

Pri profil fotoğrafı

Pri1 ay önce

Pleasure working on the launch video! 🔥

aiden brotman profil fotoğrafı

aiden brotman1 ay önce

even more affordable and accurate? what can't extend do?

Darren Chow profil fotoğrafı

Darren Chow1 ay önce

@chaneratextend extend ml team is so cracked

Kushal Byatnal profil fotoğrafı

Kushal Byatnal1 ay önce

@chaneratextend wait until they see next weeks launch

bo lau profil fotoğrafı

bo lau1 ay önce

this is huge

Kushal Byatnal profil fotoğrafı

Kushal Byatnal1 ay önce

don't tell them what's coming next week

scott profil fotoğrafı

scott1 ay önce

mr krabs approves

Hansen Qian profil fotoğrafı

Hansen Qian1 ay önce

Congrats!!! Big accomplishment

Kushal Byatnal profil fotoğrafı

Kushal Byatnal1 ay önce

ty hansen!

Mike Bedarev profil fotoğrafı

Mike Bedarev1 ay önce

The Pareto Frontier had no idea what our next move was

Victor Dubeux profil fotoğrafı

Victor Dubeux1 ay önce

light & cheap, let's go!!!!

James Russo profil fotoğrafı

James Russo1 ay önce

Congrats on the launch!

Kushal Byatnal profil fotoğrafı

Kushal Byatnal1 ay önce

thanks James!

Jordan Meyer profil fotoğrafı

Jordan Meyer1 ay önce

they said it couldn't be done

Kushal Byatnal profil fotoğrafı

Kushal Byatnal1 ay önce

that's because they've never met @ameyaajoshi and @joon_kim96

Cody profil fotoğrafı

Cody1 ay önce

ACORD forms?

Kushal Byatnal profil fotoğrafı

Kushal Byatnal1 ay önce

yup! very common use case for us

Pierre-Louis Vouteau profil fotoğrafı

Pierre-Louis Vouteau1 ay önce

@ycombinator Congrats!!

Kushal Byatnal profil fotoğrafı

Kushal Byatnal1 ay önce

@ycombinator thank you! we are big fans of @Ooakdata btw 🔥

Guhan profil fotoğrafı

Guhan1 ay önce

Congrats to your team. Cost really matters; I am currently using LlamaParse. Would you be able to explain how your product works? Is it agentic because I am working on legal workflows that need pure reflection rather than interpretation?

John Wei profil fotoğrafı

John Wei1 ay önce

congrats, lfg!

Arvind profil fotoğrafı

Arvind1 ay önce

🔥 always cooking

Kushal Byatnal profil fotoğrafı

Kushal Byatnal1 ay önce

a true chef never stops

Gordon Wintrob profil fotoğrafı

Gordon Wintrob1 ay önce

Congrats Kushal and team!! ⚡️⚡️⚡️

Launch Archive profil fotoğrafı

Launch Archive1 ay önce

Light Parse offers a cost-effective solution for document parsing, making it easier for developers to manage large data. Congrats @kushalbyatnal on #2 on Launch Archive today.

Kalyan profil fotoğrafı

Kalyan1 ay önce

cheap parsing is huge - I lean on vision models to read grocery receipts in my new iOS pantry app:

Elise Tech Ai profil fotoğrafı

Elise Tech Ai1 ay önce

70% lower costs with strong benchmark performance is a huge win—efficient document parsing is getting seriously competitive.

RAZA | AI EXPLORER profil fotoğrafı

RAZA | AI EXPLORER1 ay önce

70% cheaper while maintaining strong benchmark performance is a huge win for document AI.

Luke Beck profil fotoğrafı

Luke Beck1 ay önce

How is strikethrough detection?

SomacoSF profil fotoğrafı

SomacoSF1 ay önce

add municipal permits by zipcode. meaning oull all munucipal permit data by zipcode so you can see what is being permitted for build. then you can extrapolate from there

Alice The Ai Expert profil fotoğrafı

Alice The Ai Expert1 ay önce

Huge win 70% cheaper with SOTA accuracy is exactly what teams need!

Lokesh Sanapalli profil fotoğrafı

Lokesh Sanapalli1 ay önce

Congratulations! How does it compare with firecrawl pdf inspector?

Internet Labs profil fotoğrafı

Internet Labs1 ay önce

At $0.00625 a page, I’m about to parse every obnoxiously long CVS receipt I’ve ever received just to test the table models. Huge win for the builders.

Saurabh Jambure profil fotoğrafı

Saurabh Jambure1 ay önce

Interesting

Romero Rodrigues profil fotoğrafı

Romero Rodrigues1 ay önce

70 percent cheaper on the page is the actual product. Doc AI just became a cost line, not a seat. Whoever owns the parse at a fraction of a cent owns the agent workflow that sits on top of it.

Haseeb profil fotoğrafı

Haseeb1 ay önce

70% cheaper gets especially interesting once parsing becomes a default step in every document workflow.

Modelplane profil fotoğrafı

Modelplane1 ay önce

The 70% cheaper claim is bold—curious how it holds up on complex layouts like tables or handwriting, where parsing costs usually spike. Databricks OfficeQA Pro is a solid benchmark, but real-world docs always find the edge cases.

Dinesh | Umbra profil fotoğrafı

Dinesh | Umbra1 ay önce

the real ai race is becoming a race to zero inference cost

Vakada Rohit profil fotoğrafı

Vakada Rohit1 ay önce

70% cheaper without sacrificing accuracy is a seriously impressive improvement.

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Kushal Byatnal

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Astra is awesome, but if you're using it specifically to parse and extract a massive volume of documents, you will break the bank (10c+ per page on parsing and extraction!) We've added a native LlamaParse connector to ChatGPT that gives the model access to high-quality document OCR and extraction at the pareto frontier of accuracy and cost. It contains native access to our parse and extract endpoints. Upload your most complex documents, and you can do the following: * digitalize it into extremely accurate context for any QA task * perform large-scale extraction from a set of documents with accurate grounding and citations Offload the higher-level reasoning to Astra, leave specialized doc parsing and extraction to us. ChatGPT plugin: Come sign up here:

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i'm flattered by the shoutout, but there's no free lunch for document parsing Cohere parse starts at $1.50 / 1k pages, which is indeed on the cheaper end of doc OCR solutions, but it scores ~50% when averaged across all 5 ParseBench dimensions: * It lacks high-quality visual grounding, doing coarse segmentation for tables and images, but lacking finer-grained bboxes at the word/cell/line-level. this is quite important for any sensitive AI application that requires precise citations back to the source. * It lacks general visual / chart parsing capabilities, which are prevalent across finance, manufacturing, healthcare, and more * to its credit, it does a decent job over tables (87%) compared to other comparable solutions at the price point our cost-effective mode in LlamaParse starts at 0.375c / page and is more consistently strong across all dimensions. also there's various ways to bring it down with volume discounts if you're interested in learning more about various document parsing modes and comparing tradeoffs in price/accuracy, come talk to us! LlamaParse:

i'm flattered by the shoutout, but there's no free lunch for document parsing Cohere parse starts at $1.50 / 1k pages, which is indeed on the cheaper end of doc OCR solutions, but it scores ~50% when averaged across all 5 ParseBench dimensions: * It lacks high-quality visual grounding, doing coarse segmentation for tables and images, but lacking finer-grained bboxes at the word/cell/line-level. this is quite important for any sensitive AI application that requires precise citations back to the source. * It lacks general visual / chart parsing capabilities, which are prevalent across finance, manufacturing, healthcare, and more * to its credit, it does a decent job over tables (87%) compared to other comparable solutions at the price point our cost-effective mode in LlamaParse starts at 0.375c / page and is more consistently strong across all dimensions. also there's various ways to bring it down with volume discounts if you're interested in learning more about various document parsing modes and comparing tradeoffs in price/accuracy, come talk to us! LlamaParse:

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Parsing documents with AI agents just got a lot more seamless🚀 We've rebuilt the LlamaParse MCP server to handle your document processing workflows, and you can connect it today to any MCP-compatible client at 🌐 Once connected, you'll be able to: 📁 Parse documents into clean markdown 🔍 Classify files against your own categories ✂️ Split long documents into labelled sections ⬆️ Upload files via URL or a browser-based upload flow Building a production MCP server surfaced some non-obvious challenges: getting auth to align with an existing platform identity system using WorkOS, working around MCP's lack of built-in file upload support, and making deployments, rate limiting and observability feel native with Vercel and . We wrote up all of it, from the OAuth flow, to the token-based upload design, to the tradeoffs we hit along the way📝 📚 Read the full blog: 👩‍💻 GitHub repository:

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35,890 görüntüleme • 1 yıl önce

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Today, we're announcing our Series B: $200M at a $3.1B valuation, and the release of Arena's Alignment Index. AI is advancing faster than our ability to evaluate it. The world needs a neutral third party to measure how safe and aligned AI actually is once it's in the hands of real people. That's the role Arena is stepping into today. The Alignment Index is built from real-world agent traces, starting with three signals: Unauthorized Action, False Attribution, and Deceptive Completion, with results for over 20 frontier models launching today. Hear more from our CEO Anastasios Nikolas Angelopoulos below, and read the full breakdown, and more details about our company growth in thread. This funding will allow us to go further on safety signals, agentic capabilities, and modalities. It will also allow us to grow the company to match the scale of our mission. Today, we’re still a nimble team of 90, and we're looking for people to join us across research, product, engineering, and more. Come build with us. As AI gets more capable and autonomous, understanding how well it can safely and effectively work for people only matters more. That's what Arena is built to measure and advance. Thank you to our community, our investors, and everyone building with us! Take a look at our open roles if you want to join us (link in bio).

Arena.ai

114,809 görüntüleme • 11 gün önce

The MiniMax M2 model is mind-blowing! It's open-source. It outperforms Gemini 2.5, Claude 4.1, and Qwen3 across coding and tool-use benchmarks. Right now, it's one of the world's top 5 models in intelligence! And here is the best part: Claude is one of the best models you can use today, and MiniMax M2 costs only 8% of that! It's smaller, faster, and cheaper. Extremely efficient at using tokens. Minimax M2's biggest strength: High agentic capabilities. The model can plan and execute complex multi-tool workflows. It's reliable and very robust at executing long-horizon tool chains. In summary: • Low latency • Very cheap • Excels at agentic tasks • Open-source The model currently powers the MiniMax Agent and is available for a free global trial. You can access MiniMax M2's API here: To access the agent: And here is the MiniMax website: Thanks to the MiniMax team for showing me the ropes and partnering with me on this post.

The MiniMax M2 model is mind-blowing! It's open-source. It outperforms Gemini 2.5, Claude 4.1, and Qwen3 across coding and tool-use benchmarks. Right now, it's one of the world's top 5 models in intelligence! And here is the best part: Claude is one of the best models you can use today, and MiniMax M2 costs only 8% of that! It's smaller, faster, and cheaper. Extremely efficient at using tokens. Minimax M2's biggest strength: High agentic capabilities. The model can plan and execute complex multi-tool workflows. It's reliable and very robust at executing long-horizon tool chains. In summary: • Low latency • Very cheap • Excels at agentic tasks • Open-source The model currently powers the MiniMax Agent and is available for a free global trial. You can access MiniMax M2's API here: To access the agent: And here is the MiniMax website: Thanks to the MiniMax team for showing me the ropes and partnering with me on this post.

Santiago

91,263 görüntüleme • 11 ay önce

AI agents are changing how we work and collaborate, but documents are still stuck in the pre-AI era. Agents can now create beautiful, interactive decks, dashboards, and reports in HTML. But the moment you want to collaborate on them, everything breaks down. Google Docs wasn’t built for rich, interactive HTML or AI agents. Feedback gets scattered across Slack and email. Every edit means going back to the agent, generating a new file, and sharing another link. No real-time collaboration. No single source of truth. We built SlashDocs to fix this. Think Google Docs, but built for AI agents and the documents they create. Host HTML and Markdown in one central hub. Share documents, manage permissions, and comment directly on the content, just like Google Docs. Agents can read feedback and update the same document in real time. No duplicate files, no endless back-and-forth, and everyone stays in sync. What started as a small internal experiment in one team has now scaled across Razorpay. 28,000+ documents created. Over 50% of all documents generated at Razorpay now live on SlashDocs. And today, we’re opening it up to everyone. Self-hosted. Agent-agnostic. Fully open source. AI agents have changed how we create documents. It’s time we changed how we collaborate on them. Try it:

AI agents are changing how we work and collaborate, but documents are still stuck in the pre-AI era. Agents can now create beautiful, interactive decks, dashboards, and reports in HTML. But the moment you want to collaborate on them, everything breaks down. Google Docs wasn’t built for rich, interactive HTML or AI agents. Feedback gets scattered across Slack and email. Every edit means going back to the agent, generating a new file, and sharing another link. No real-time collaboration. No single source of truth. We built SlashDocs to fix this. Think Google Docs, but built for AI agents and the documents they create. Host HTML and Markdown in one central hub. Share documents, manage permissions, and comment directly on the content, just like Google Docs. Agents can read feedback and update the same document in real time. No duplicate files, no endless back-and-forth, and everyone stays in sync. What started as a small internal experiment in one team has now scaled across Razorpay. 28,000+ documents created. Over 50% of all documents generated at Razorpay now live on SlashDocs. And today, we’re opening it up to everyone. Self-hosted. Agent-agnostic. Fully open source. AI agents have changed how we create documents. It’s time we changed how we collaborate on them. Try it:

Shashank Kumar

168,948 görüntüleme • 7 gün önce

We're announcing the most significant new strategic development since the start of the Customer Agent category: Fin now has specialized roles. And starting today anyone can sign up for and deploy the Fin Sales Role in minutes. Fin is now by far the very best sales agent on the market, and it's been live with some of the most innovative digital brands for months, conversing with thousands of prospects, aiding discovery, building pipeline, booking meetings and starting trials. We started the Customer Agent category with a focus on service by launching Fin three months after the launch of ChatGPT. Since then Fin has continued to lead the space, today delivering over 2 million resolutions a week for over 8k customers, including Anthropic, DoorDash, Snowflake, Asana, Mercury, Polymarket and many more exceptional brands. But our vision for Fin has always stretched far beyond service. And for the past 6 months we’ve been building Fin as a single Customer Agent, delivering a seamless experience across all stages of the customer lifecycle. We do not believe customers or businesses will want multiple agents for different parts of the lifecycle—because the agents won’t have shared memory and goals, and will have to compete with each other, delivering a poor customer experience and sub-optimal business results. We’ve more roles to come, including another dropping in two weeks. For now please watch our launch video or visit our launch page to learn more: fin DOT ai SLASH sales

We're announcing the most significant new strategic development since the start of the Customer Agent category: Fin now has specialized roles. And starting today anyone can sign up for and deploy the Fin Sales Role in minutes. Fin is now by far the very best sales agent on the market, and it's been live with some of the most innovative digital brands for months, conversing with thousands of prospects, aiding discovery, building pipeline, booking meetings and starting trials. We started the Customer Agent category with a focus on service by launching Fin three months after the launch of ChatGPT. Since then Fin has continued to lead the space, today delivering over 2 million resolutions a week for over 8k customers, including Anthropic, DoorDash, Snowflake, Asana, Mercury, Polymarket and many more exceptional brands. But our vision for Fin has always stretched far beyond service. And for the past 6 months we’ve been building Fin as a single Customer Agent, delivering a seamless experience across all stages of the customer lifecycle. We do not believe customers or businesses will want multiple agents for different parts of the lifecycle—because the agents won’t have shared memory and goals, and will have to compete with each other, delivering a poor customer experience and sub-optimal business results. We’ve more roles to come, including another dropping in two weeks. For now please watch our launch video or visit our launch page to learn more: fin DOT ai SLASH sales

Eoghan McCabe

73,833 görüntüleme • 6 ay önce

Today, we’re launching Prodigy Research (YC S26), the frontier AI trading research lab. We’re training the world’s best foundation model for quantitative finance. Our AI quant outperforms a top 10% trader at Jane Street, and we've already achieved more than 100% returns in live trading over our YC batch, while major indices were flat or down. Prodigy’s model beats Claude Fable and GPT-5.6 Sol at autonomous quant research. Our founders are brothers who have built strategies generating billions in PnL on a Jane Street trading desk, trained SoTA foundation models at Google DeepMind, and shipped cutting-edge AI to billions of users at Apple. A new era has arrived in the world of quantitative finance. To learn more, contact founders@prodigy.markets. Prodigy Research (YC S26)

Today, we’re launching Prodigy Research (YC S26), the frontier AI trading research lab. We’re training the world’s best foundation model for quantitative finance. Our AI quant outperforms a top 10% trader at Jane Street, and we've already achieved more than 100% returns in live trading over our YC batch, while major indices were flat or down. Prodigy’s model beats Claude Fable and GPT-5.6 Sol at autonomous quant research. Our founders are brothers who have built strategies generating billions in PnL on a Jane Street trading desk, trained SoTA foundation models at Google DeepMind, and shipped cutting-edge AI to billions of users at Apple. A new era has arrived in the world of quantitative finance. To learn more, contact [email protected]. Prodigy Research (YC S26)

Michael Wang

1,941,743 görüntüleme • 2 ay önce

SITUATION EXPLAINED: Grok 4.7 beats GPT-6 Astra on real-world tasks, and it's 5x cheaper. • Same price as 4.6, $2 input and $6 output per million tokens, well under Sol, Fable, and Astra • A new larger base model, which Elon has put at 2.1 trillion parameters, up from 1.5 trillion, with extra training on SpaceX data • Trained to natively understand the Grok Bot harness, the same move Anthropic made with Claude Code • Leads on electrical engineering and Harvey's legal benchmark, beats Astra Max on GDPval, trails on software engineering • Elon Musk: "Grok 4.7 places SpaceXAI as third after Anthropic and OpenAI for agentic coding. When factoring in that Grok is significantly faster and lower cost, it's a great choice for your everyday workhorse" Theo Jaffee: "SpaceX has a huge amount of data on real world hardware problems, real world engineering. So I bet Grok models are going to be better at rocketry engineering than any of the other models out there."

SITUATION EXPLAINED: Grok 4.7 beats GPT-6 Astra on real-world tasks, and it's 5x cheaper. • Same price as 4.6, $2 input and $6 output per million tokens, well under Sol, Fable, and Astra • A new larger base model, which Elon has put at 2.1 trillion parameters, up from 1.5 trillion, with extra training on SpaceX data • Trained to natively understand the Grok Bot harness, the same move Anthropic made with Claude Code • Leads on electrical engineering and Harvey's legal benchmark, beats Astra Max on GDPval, trails on software engineering • Elon Musk: "Grok 4.7 places SpaceXAI as third after Anthropic and OpenAI for agentic coding. When factoring in that Grok is significantly faster and lower cost, it's a great choice for your everyday workhorse" Theo Jaffee: "SpaceX has a huge amount of data on real world hardware problems, real world engineering. So I bet Grok models are going to be better at rocketry engineering than any of the other models out there."

MTS

18,190 görüntüleme • 28 gün önce

We just launched Telnyx Decision Models in beta. AI agents make small judgment calls on every run. Is this caller asking for a refund? Which team owns this ticket? Is this incident urgent enough to page someone? Decision Models answers them in one API call. Send the context and up to 64 typed questions, and get typed answers back: a choice, a yes/no score, or a rating on a scale. Your code branches on the answer. There's no free text to parse. We are offering 2 models, chosen per request: → Decision Flash, the default, for high-volume routing and triage at the lowest cost and latency. $0.035 per 1M input tokens. → Decision Pro, for decisions that depend on long context, like a full support history or a long call transcript. $0.20 per 1M input tokens. Output tokens are free on both. The API is TypeSafe-compatible, so teams already using the TypeSafe SDK can point it at Telnyx with a new base URL and model name. It runs on the same API key as Telnyx voice, speech-to-text, and inference, so you don’t need to join any waitlist or go through extra setup to access it. Try it today:

We just launched Telnyx Decision Models in beta. AI agents make small judgment calls on every run. Is this caller asking for a refund? Which team owns this ticket? Is this incident urgent enough to page someone? Decision Models answers them in one API call. Send the context and up to 64 typed questions, and get typed answers back: a choice, a yes/no score, or a rating on a scale. Your code branches on the answer. There's no free text to parse. We are offering 2 models, chosen per request: → Decision Flash, the default, for high-volume routing and triage at the lowest cost and latency. $0.035 per 1M input tokens. → Decision Pro, for decisions that depend on long context, like a full support history or a long call transcript. $0.20 per 1M input tokens. Output tokens are free on both. The API is TypeSafe-compatible, so teams already using the TypeSafe SDK can point it at Telnyx with a new base URL and model name. It runs on the same API key as Telnyx voice, speech-to-text, and inference, so you don’t need to join any waitlist or go through extra setup to access it. Try it today:

Telnyx

866,183 görüntüleme • 25 gün önce

We’re excited to officially launch LlamaParse, the first genAI-native document parsing solution. Not only is it better at parsing out images/tables/charts 📊📈 than virtually every other parser, it is now steerable through natural language instructions - output the document in whatever format you desire! It is also the only parsing solution that seamlessly allows you to build accurate RAG over complex documents, free of hallucinations 🔥 We launched it in private preview a few weeks ago and hit 2k users, 1M total PDF pages parsed. And now it’s better than ever. LlamaParse contains the following killer features: ✅ SOTA table/chart extraction ✅ Seamless integration with LlamaIndex 🦙 advanced RAG/agents ✅✨ Natural language Parsing Instructions ✅✨JSON mode and image extraction ✅✨Support for ~10 document types (.pdf, .pptx, .docx, .xml) and more Our pricing is simple: 1k free per day, and additional pages at 0.3c a page, or $3 for 1k pages. If you want advanced document RAG and/or private deployments, come get in touch with us to chat about LlamaCloud. Check out our full blog post here: LlamaParse client repo: Signup at 🦙☁️: Come talk to us:

We’re excited to officially launch LlamaParse, the first genAI-native document parsing solution. Not only is it better at parsing out images/tables/charts 📊📈 than virtually every other parser, it is now steerable through natural language instructions - output the document in whatever format you desire! It is also the only parsing solution that seamlessly allows you to build accurate RAG over complex documents, free of hallucinations 🔥 We launched it in private preview a few weeks ago and hit 2k users, 1M total PDF pages parsed. And now it’s better than ever. LlamaParse contains the following killer features: ✅ SOTA table/chart extraction ✅ Seamless integration with LlamaIndex 🦙 advanced RAG/agents ✅✨ Natural language Parsing Instructions ✅✨JSON mode and image extraction ✅✨Support for ~10 document types (.pdf, .pptx, .docx, .xml) and more Our pricing is simple: 1k free per day, and additional pages at 0.3c a page, or $3 for 1k pages. If you want advanced document RAG and/or private deployments, come get in touch with us to chat about LlamaCloud. Check out our full blog post here: LlamaParse client repo: Signup at 🦙☁️: Come talk to us:

LlamaIndex 🦙

143,198 görüntüleme • 2 yıl önce

We’re launching Optima. Now anyone can create a custom benchmark for their use case, leveraging Artificial Analysis’ leading research and platform Building and running benchmarks is difficult. We have distilled Artificial Analysis’ research and experience developing benchmarks into Optima, a new platform for benchmarking models on your own workloads and comparing performance, speed and cost efficiency. Optima allows you to find the best model for your task, or an equally performant alternative to your current setup at 10x lower cost or time per task. We’ve integrated Artificial Analysis' research and experience in benchmarks across the Optima workflow: ➤ Build benchmarks based on your own data and use cases: There are three ways to build a benchmark with Optima. Upload an existing evaluation dataset from your own files or Hugging Face, or import agent traces from platforms including Arize AI, Braintrust and langfuse.com. Install the Optima skill to build a benchmark using context from your coding environment and previous sessions. Or simply describe your use case and provide example inputs and outputs, and Optima will build the benchmark for you ➤ Run across the latest models: Run the same benchmark across leading models in a single click, and keep your leaderboard up to date as soon as new models are released ➤ Bring Artificial Analysis grading to your own benchmark: Evaluate responses against objective rubric criteria or using the same pairwise judging approach used for Artificial Analysis benchmarks including GDPval-AA and AA-Briefcase. For pairwise judging, select your preferred responses from a sample and Optima uses those preferences to rank models across your test set ➤ Compare performance, cost and time efficiency: Optima measures more than model performance. Cost per Task and Time per Task are tracked alongside benchmark scores, with category-level results and support for custom metrics, allowing you to compare the tradeoffs between models for your specific use case Ahead of launch, here are examples questions our beta testers answered with Optima: ➤ Which model can save me 10x the cost without a meaningful decrease in quality for my finance & accounting agent? ➤ Which model best matches the writing style of lawyers for my legal agent? ➤ Which model can best identify different elements in my custom image dataset? Optima is available today. Build your own benchmark at

We’re launching Optima. Now anyone can create a custom benchmark for their use case, leveraging Artificial Analysis’ leading research and platform Building and running benchmarks is difficult. We have distilled Artificial Analysis’ research and experience developing benchmarks into Optima, a new platform for benchmarking models on your own workloads and comparing performance, speed and cost efficiency. Optima allows you to find the best model for your task, or an equally performant alternative to your current setup at 10x lower cost or time per task. We’ve integrated Artificial Analysis' research and experience in benchmarks across the Optima workflow: ➤ Build benchmarks based on your own data and use cases: There are three ways to build a benchmark with Optima. Upload an existing evaluation dataset from your own files or Hugging Face, or import agent traces from platforms including Arize AI, Braintrust and langfuse.com. Install the Optima skill to build a benchmark using context from your coding environment and previous sessions. Or simply describe your use case and provide example inputs and outputs, and Optima will build the benchmark for you ➤ Run across the latest models: Run the same benchmark across leading models in a single click, and keep your leaderboard up to date as soon as new models are released ➤ Bring Artificial Analysis grading to your own benchmark: Evaluate responses against objective rubric criteria or using the same pairwise judging approach used for Artificial Analysis benchmarks including GDPval-AA and AA-Briefcase. For pairwise judging, select your preferred responses from a sample and Optima uses those preferences to rank models across your test set ➤ Compare performance, cost and time efficiency: Optima measures more than model performance. Cost per Task and Time per Task are tracked alongside benchmark scores, with category-level results and support for custom metrics, allowing you to compare the tradeoffs between models for your specific use case Ahead of launch, here are examples questions our beta testers answered with Optima: ➤ Which model can save me 10x the cost without a meaningful decrease in quality for my finance & accounting agent? ➤ Which model best matches the writing style of lawyers for my legal agent? ➤ Which model can best identify different elements in my custom image dataset? Optima is available today. Build your own benchmark at

Artificial Analysis

133,651 görüntüleme • 2 ay önce

Introducing Slashspace✨ A canvas first agentic harness for power AI users. We are trying to replace all UIs with one interface for the AI era. For the past 18 months i've been building this and it's finally taking shape towards being the ultimate AI interface for a power AI user. We iterated on the canvas experience many times. Started as RabbitHoles AI with node based conversations. Slashspace is now evolving towards canvas-as-context app - The nodes are all agentic, and you don't need to draw connections and do the manual work. Today we're launching on Product Hunt 😸, and your support will mean a lot! As a solo bootstrapped founder, I've invested a lot into this product, so please show some love! 👀 Some feature highlights • Canvas with multi model, multi modal chats. • Local storage with all canvases stored in your folder system • Agentic nodes that can see all the context on the canvas. • Connect to Cursor sdk and use the cursor's harness locally, but chat on the canvas • Access to all the Composio supported tools on the canvas. s/o KP for hunting our PH launch. s/o to Tibo, Tony Dinh, David & HRG gang for forcing the clarity out of me on repositioning the product

Introducing Slashspace✨ A canvas first agentic harness for power AI users. We are trying to replace all UIs with one interface for the AI era. For the past 18 months i've been building this and it's finally taking shape towards being the ultimate AI interface for a power AI user. We iterated on the canvas experience many times. Started as RabbitHoles AI with node based conversations. Slashspace is now evolving towards canvas-as-context app - The nodes are all agentic, and you don't need to draw connections and do the manual work. Today we're launching on Product Hunt 😸, and your support will mean a lot! As a solo bootstrapped founder, I've invested a lot into this product, so please show some love! 👀 Some feature highlights • Canvas with multi model, multi modal chats. • Local storage with all canvases stored in your folder system • Agentic nodes that can see all the context on the canvas. • Connect to Cursor sdk and use the cursor's harness locally, but chat on the canvas • Access to all the Composio supported tools on the canvas. s/o KP for hunting our PH launch. s/o to Tibo, Tony Dinh, David & HRG gang for forcing the clarity out of me on repositioning the product

Praneeth Pike

49,563 görüntüleme • 4 ay önce

It's not every day I can share news that will completely transform your workday 🚀 Over the last couple of years, we’ve seen how AI can transform our personal lives, but this hasn’t been replicated at work—yet. That's all changing today with Amazon Quick Suite, a new agentic AI experience invented at Amazon and tested by tens of thousands of our employees. Amazon Quick Suite is your AI teammate that helps you cut through the noise of repetitive tasks and fragmented information. It can pull data from your documents (in SharePoint, Google Drive, OneDrive, etc.), emails (like Outlook), messages (like Slack), enterprise apps (like Salesforce and ServiceNow), data warehouses (like Redshift, Snowflake, Databricks, and many more) and the web so you can get work done faster. With support for MCP and partners like Workato and Zapier, you can connect data from 1000s of apps with Quick. There’s a lot more to Quick than I can cover in this post alone—my latest blog has all the details. Give Quick a spin today and see why teams at Amazon are loving this! ➡️

It's not every day I can share news that will completely transform your workday 🚀 Over the last couple of years, we’ve seen how AI can transform our personal lives, but this hasn’t been replicated at work—yet. That's all changing today with Amazon Quick Suite, a new agentic AI experience invented at Amazon and tested by tens of thousands of our employees. Amazon Quick Suite is your AI teammate that helps you cut through the noise of repetitive tasks and fragmented information. It can pull data from your documents (in SharePoint, Google Drive, OneDrive, etc.), emails (like Outlook), messages (like Slack), enterprise apps (like Salesforce and ServiceNow), data warehouses (like Redshift, Snowflake, Databricks, and many more) and the web so you can get work done faster. With support for MCP and partners like Workato and Zapier, you can connect data from 1000s of apps with Quick. There’s a lot more to Quick than I can cover in this post alone—my latest blog has all the details. Give Quick a spin today and see why teams at Amazon are loving this! ➡️

Swami Sivasubramanian

11,011 görüntüleme • 1 yıl önce

⚡️We are excited to announce that our new no-code Enterprise Platform is NOW available in private beta! As RAG apps advance from prototype to production we’ve been overwhelmed by requests for an enterprise grade solution to provide these applications with the data they need. Designed to make it easy to get your data #RAGready, our Platform can preprocess more than 25 file types and soon will be fully #multimodal, also able to ingest audio, video and image files. We ship with a baseline suite of source connectors, including Amazon Web Services S3, Microsoft Azure Blob Storage, OneDrive, SFTP, Databricks Delta Table, Google Drive, Salesforce, Elastic, OpenSearch, and Google Cloud storage with many more fast following. Platform transforms your documents into a standardized JSON schema, broken down into semantically coherent elements allowing you to reconstruct your document in the manner most useful to you. Want only the narrative text but not the headers and footers? This is entirely configurable through the UI. Additionally, we generate more than 30 types of metadata for each element to make it easy to curate the data being written downstream and to support metadata filtering during retrieval. Smart chunking and the ability to choose from a range of embedding models are in from launch, delivering a turnkey solution for chunk and embedding experimentation. As for destination connectors, we've got that covered too, with Amazon Web Services S3, Pinecone, Chroma , Weaviate AI Database, Google Cloud storage, MongoDB, Microsoft Azure cognitive search, PostgreSQL, Elastic, OpenSearch, and Databricks Delta Table. And of course, all of this can be scheduled to keep your data continuously hydrated. The private-beta is live today! Sign-up to get access and come build the future of LLM data foundations with us: 🚀 #ETLforLLMs #AI #DataPreprocessing #DataScience #DataTransformation #LLMs #ETL #ML #PreppingData #MachineLearning #RAG #Engineer #Unstructured #Unstructuredio #RetrievalAugmentedGeneration #multimodal #AIJobs

⚡️We are excited to announce that our new no-code Enterprise Platform is NOW available in private beta! As RAG apps advance from prototype to production we’ve been overwhelmed by requests for an enterprise grade solution to provide these applications with the data they need. Designed to make it easy to get your data #RAGready, our Platform can preprocess more than 25 file types and soon will be fully #multimodal, also able to ingest audio, video and image files. We ship with a baseline suite of source connectors, including Amazon Web Services S3, Microsoft Azure Blob Storage, OneDrive, SFTP, Databricks Delta Table, Google Drive, Salesforce, Elastic, OpenSearch, and Google Cloud storage with many more fast following. Platform transforms your documents into a standardized JSON schema, broken down into semantically coherent elements allowing you to reconstruct your document in the manner most useful to you. Want only the narrative text but not the headers and footers? This is entirely configurable through the UI. Additionally, we generate more than 30 types of metadata for each element to make it easy to curate the data being written downstream and to support metadata filtering during retrieval. Smart chunking and the ability to choose from a range of embedding models are in from launch, delivering a turnkey solution for chunk and embedding experimentation. As for destination connectors, we've got that covered too, with Amazon Web Services S3, Pinecone, Chroma , Weaviate AI Database, Google Cloud storage, MongoDB, Microsoft Azure cognitive search, PostgreSQL, Elastic, OpenSearch, and Databricks Delta Table. And of course, all of this can be scheduled to keep your data continuously hydrated. The private-beta is live today! Sign-up to get access and come build the future of LLM data foundations with us: 🚀 #ETLforLLMs #AI #DataPreprocessing #DataScience #DataTransformation #LLMs #ETL #ML #PreppingData #MachineLearning #RAG #Engineer #Unstructured #Unstructuredio #RetrievalAugmentedGeneration #multimodal #AIJobs

Unstructured

21,913 görüntüleme • 2 yıl önce