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The token maxxing hype is over 80% waste. We pulled data from our customer spend analysis across 2,444 companies and found something uncomfortable. For every $1 spent on AI tokens, only $0.18 actually reaches the product. The other $0.82 breaks down like this: $0.44 goes into fixing bugs the... show more

Aiswarya Sankar

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14,708 просмотров • 2 месяцев назад •via X (Twitter)

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$THIS GUY AUDITED 926 CLAUDE CODE SESSIONS AND FOUND MOST OF THE TOKEN WASTE WAS ON HIS SIDE everyone is blaming anthropic for the limits, so he decided to actually look at the data 858 sessions, 18,903 turns, and $1,619 estimated spend across 33 days here's what he found: 1\ one default setting was burning 14,000 tokens per turn Claude Code loads the full JSON schema for every tool into context at session start. whether you use them or not. 20,000 tokens of tool definitions sitting there on every single turn. the fix: one line in your settings.json "ENABLE_TOOL_SEARCH": "true" context dropped from 45K to 20K instantly. across 858 sessions that one setting was wasting an estimated 264 million tokens 2\ cache expiry is the single biggest waste 54% of his turns came after a 5+ minute idle gap. every one of those turns re-processed the entire conversation at full price which caused a 10x cost jump you go grab coffee. come back 5 minutes later. type your next message. everything rebuilds from scratch. the context didn't change. you didn't change. the cache just expired. 12.3 million tokens wasted on idle gaps alone 3\ 42 skills loaded. 19 of them used twice or less across 858 sessions. every one of those skill schemas sat in context on every turn eating tokens for nothing. 4\ 1,122 redundant file reads where the same file was read 3+ times one session read the same file 33 times. he ALSO built a full token auditor dashboard that shows you exactly where your waste is coming from 19 charts, opens in your browser, free AND open source$

THIS GUY AUDITED 926 CLAUDE CODE SESSIONS AND FOUND MOST OF THE TOKEN WASTE WAS ON HIS SIDE everyone is blaming anthropic for the limits, so he decided to actually look at the data 858 sessions, 18,903 turns, and $1,619 estimated spend across 33 days here's what he found: 1\ one default setting was burning 14,000 tokens per turn Claude Code loads the full JSON schema for every tool into context at session start. whether you use them or not. 20,000 tokens of tool definitions sitting there on every single turn. the fix: one line in your settings.json "ENABLE_TOOL_SEARCH": "true" context dropped from 45K to 20K instantly. across 858 sessions that one setting was wasting an estimated 264 million tokens 2\ cache expiry is the single biggest waste 54% of his turns came after a 5+ minute idle gap. every one of those turns re-processed the entire conversation at full price which caused a 10x cost jump you go grab coffee. come back 5 minutes later. type your next message. everything rebuilds from scratch. the context didn't change. you didn't change. the cache just expired. 12.3 million tokens wasted on idle gaps alone 3\ 42 skills loaded. 19 of them used twice or less across 858 sessions. every one of those skill schemas sat in context on every turn eating tokens for nothing. 4\ 1,122 redundant file reads where the same file was read 3+ times one session read the same file 33 times. he ALSO built a full token auditor dashboard that shows you exactly where your waste is coming from 19 charts, opens in your browser, free AND open source

Om Patel

298,746 просмотров • 3 месяцев назад

WHOS READY TO RUN IT BACK TURBO?! Introducing FUTURE DAO! So we got DAOs ✅ We got Prediction markets ✅ We got Ai ✅ We got meme-coins✅ We got a pre sale✅ Prediction markets have been a bit boring to me TBH, I WANT TO HIT 100Xs not like do 10hrs of research on Greenland politics or something. So I created this DAO to give people some leverage, and make it fun. Basically how it works, is we do a pre sale, people get tokens, the rest of the token supply goes into the DAO. Like a classic DAOs fun but you can redeem the AUM at any time,(but you incur a penalty) . You have the AUM, and our proprietary Ai does the trading for you. Just hold the token and watch it go up(hopefully), the Ai trades with guidance from Token holder votes. The token holders also get to vote/guide the Ai into outcomes they think will benefit the DAO/token. AUM Goes up. Token Goes up. Everyone happy. Or AUM flat/Down. Token Goes down. People Redeem and get % of their money back. better than zeroing out on meme coins. The token will all be launched via a semi-gated pre sale, And using my own method of launching to showcase how its done and to ensure there is a good group of "seed DAO" members to help the Ai. it will run 8 weeks, and people can decide to shut it down and get $$ back. or keep it going forever. end of the day this is a meme coin guys, but I think its a cool experiment.

WHOS READY TO RUN IT BACK TURBO?! Introducing FUTURE DAO! So we got DAOs ✅ We got Prediction markets ✅ We got Ai ✅ We got meme-coins✅ We got a pre sale✅ Prediction markets have been a bit boring to me TBH, I WANT TO HIT 100Xs not like do 10hrs of research on Greenland politics or something. So I created this DAO to give people some leverage, and make it fun. Basically how it works, is we do a pre sale, people get tokens, the rest of the token supply goes into the DAO. Like a classic DAOs fun but you can redeem the AUM at any time,(but you incur a penalty) . You have the AUM, and our proprietary Ai does the trading for you. Just hold the token and watch it go up(hopefully), the Ai trades with guidance from Token holder votes. The token holders also get to vote/guide the Ai into outcomes they think will benefit the DAO/token. AUM Goes up. Token Goes up. Everyone happy. Or AUM flat/Down. Token Goes down. People Redeem and get % of their money back. better than zeroing out on meme coins. The token will all be launched via a semi-gated pre sale, And using my own method of launching to showcase how its done and to ensure there is a good group of "seed DAO" members to help the Ai. it will run 8 weeks, and people can decide to shut it down and get $$ back. or keep it going forever. end of the day this is a meme coin guys, but I think its a cool experiment.

Skely

47,630 просмотров • 6 месяцев назад

Lightspeed's Bucky Moore says the real opportunity in the AI app layer is in large industries far enough afield from where the model providers are today — and where the context engineering to get customer data into the model is extremely nuanced and messy. "I think this is kind of the elephant in the room right now — whether post-training open-source models combined with the unique user feedback you get from being an application provider is defensible enough." "That is going to be an inevitable challenge for any of these industries that hit a maturation point of AI adoption, like legal and software engineering have." "But on the other hand, there are some industries where they're very large, they're far enough afield from where the model providers are today — and probably will continue to be — and the context engineering to actually get the customer data into the model is just so messy. It requires going across different business functions, it requires a lot of hands-on forward-deployed engineering." "Those are the kind of companies that we get really excited about. Because I think being really good at that is not only defensible, but it also allows you to generate a feedback loop with your customers, where you hear a lot of their secrets. And those secrets allow you to feed that back into how you make your product better at the expense of anyone else playing in the space. Because if you're serving the customer, they're only serving you those secrets." "I think Palantir is a good example of this in the pre-AI era, and I think we're going to see many companies ascend in that same way."

Lightspeed's Bucky Moore says the real opportunity in the AI app layer is in large industries far enough afield from where the model providers are today — and where the context engineering to get customer data into the model is extremely nuanced and messy. "I think this is kind of the elephant in the room right now — whether post-training open-source models combined with the unique user feedback you get from being an application provider is defensible enough." "That is going to be an inevitable challenge for any of these industries that hit a maturation point of AI adoption, like legal and software engineering have." "But on the other hand, there are some industries where they're very large, they're far enough afield from where the model providers are today — and probably will continue to be — and the context engineering to actually get the customer data into the model is just so messy. It requires going across different business functions, it requires a lot of hands-on forward-deployed engineering." "Those are the kind of companies that we get really excited about. Because I think being really good at that is not only defensible, but it also allows you to generate a feedback loop with your customers, where you hear a lot of their secrets. And those secrets allow you to feed that back into how you make your product better at the expense of anyone else playing in the space. Because if you're serving the customer, they're only serving you those secrets." "I think Palantir is a good example of this in the pre-AI era, and I think we're going to see many companies ascend in that same way."

TBPN

46,746 просмотров • 4 месяцев назад

Liquid AI (Liquid AI) CEO Ramin Hasani (Ramin) says: "We're bringing the cost of tokens to zero." "The axis was maximizing intelligence at all costs." Foundation models need to optimize across 3 axes: 1.) Intelligence and capability 2.) Efficiency and cost 3.) Substrate: where the intelligence actually runs "Efficiency is not an afterthought. Energy is not abundant." "If you maximize intelligence at all costs, that axis alone is not going to get you to the place that you want to go." "Efficiency and cost of intelligence as a first-class citizen, and not an afterthought." "Where does this intelligence system go? AI is majorly getting hosted in data centers, but you could also bring intelligence on phones, on laptops, on airplanes, on cars." "Imagine if it's not tokens that are actually important. It's just the outcome that actually matters." "Not just thinking about foundation models as the token machines that are generating money and revenue for the foundation model companies that are useless tokens, and getting them into the place where they can actually unlock true value for enterprises."

Liquid AI (Liquid AI) CEO Ramin Hasani (Ramin) says: "We're bringing the cost of tokens to zero." "The axis was maximizing intelligence at all costs." Foundation models need to optimize across 3 axes: 1.) Intelligence and capability 2.) Efficiency and cost 3.) Substrate: where the intelligence actually runs "Efficiency is not an afterthought. Energy is not abundant." "If you maximize intelligence at all costs, that axis alone is not going to get you to the place that you want to go." "Efficiency and cost of intelligence as a first-class citizen, and not an afterthought." "Where does this intelligence system go? AI is majorly getting hosted in data centers, but you could also bring intelligence on phones, on laptops, on airplanes, on cars." "Imagine if it's not tokens that are actually important. It's just the outcome that actually matters." "Not just thinking about foundation models as the token machines that are generating money and revenue for the foundation model companies that are useless tokens, and getting them into the place where they can actually unlock true value for enterprises."

Molly O’Shea

12,816 просмотров • 23 дней назад

In our $KGN earnings call today, it sounded like it's David Shafer and I presenting but it wasn't. Our voices were AI generated and read the script. We uploaded several hours of our prior talks to train our voice model and the results were pretty incredible. This saved us a few hours preparation - valuable time we could spend working on other areas of the business. This is an example of the many one percenters across our business where AI is making us more efficient and helping the team be more productive. Our marketing team uses it to improve our content and make it more enaging. Our Engineering team uses it to get more context of our entire code base quicker and produce production ready code more rapidly. Our purchasing teams are using it for data and trend analysis to bring the latest products to our customers at even better prices. The list goes on. While the productivity gains are exciting, what's even more exciting is the pace at which things are changing and the technology is getting better!

In our $KGN earnings call today, it sounded like it's David Shafer and I presenting but it wasn't. Our voices were AI generated and read the script. We uploaded several hours of our prior talks to train our voice model and the results were pretty incredible. This saved us a few hours preparation - valuable time we could spend working on other areas of the business. This is an example of the many one percenters across our business where AI is making us more efficient and helping the team be more productive. Our marketing team uses it to improve our content and make it more enaging. Our Engineering team uses it to get more context of our entire code base quicker and produce production ready code more rapidly. Our purchasing teams are using it for data and trend analysis to bring the latest products to our customers at even better prices. The list goes on. While the productivity gains are exciting, what's even more exciting is the pace at which things are changing and the technology is getting better!

Ruslan Kogan

814,106 просмотров • 11 месяцев назад

Orynth Product Review EP11 FalkorDB is an ultra-fast graph database built for the AI era. It helps AI systems understand how information actually connects, cutting hallucinations and powering GraphRAG, AI agents, fraud detection, and more. Open source, built on the Redis foundation, and already used by companies like BMW, Mimecast, and Snowflake. Co-founded by Guy Korland, a PhD computer scientist who helped build RedisGraph before creating its successor. A real product listed on Orynth with its own token $FLKR In this eleventh episode, we break down what FalkorDB is building, the veteran founder behind it, and why an established, funded company launching a token is a rare thing to see.

Orynth Product Review EP11 FalkorDB is an ultra-fast graph database built for the AI era. It helps AI systems understand how information actually connects, cutting hallucinations and powering GraphRAG, AI agents, fraud detection, and more. Open source, built on the Redis foundation, and already used by companies like BMW, Mimecast, and Snowflake. Co-founded by Guy Korland, a PhD computer scientist who helped build RedisGraph before creating its successor. A real product listed on Orynth with its own token $FLKR In this eleventh episode, we break down what FalkorDB is building, the veteran founder behind it, and why an established, funded company launching a token is a rare thing to see.

Orynth News

25,104 просмотров • 1 месяц назад

We are excited to announce a powerful step for the future of FOMO! Taking a page out of Virtuals book on BASE, FOMO will be releasing the ability for future projects to be paired in $FOMO in the coming weeks. This is the biggest release we have ever announced. Launch your AI Agent Token + $FOMO trading pair Every individual agent token is paired with the $FOMO token in its liquidity pool. When launching an agent on you will need $FOMO tokens, which are used to create the liquidity pool. This process creates deflationary pressure for FOMO and the entire agent ecosystem. When creating your agent and token, you will have the option to pair your launch with FOMO or SOL, as our goal is not to alienate any project, but rather invite the best communities, CTO’s and builders to launch with us. If you decide to pair your project with FOMO you in turn get full marketing and dev support, once your project graduates the bonding curve and reaches Raydium. Further, as an added incentive, as our revenue grows we will be using part of the funds to support projects that have paired in FOMO. And Devs who launch tokens paired in FOMO will earn fees from their AI Agent token launch. Building the most robust agents using our framework will catapult us as one of the most prominent standards of the Solana ecosystem. Not only have we developed our own core infrastructure, but we also pull from some of the best repo’s and developer talent in all of AI, not just blockchain. Our team is comprised of 9 world class artificial intelligence engineers, PHDs in mathematics and engineering from the top companies on the cutting edge of AI. The future of AI Agents will be on Solana and we will help lead the way.

We are excited to announce a powerful step for the future of FOMO! Taking a page out of Virtuals book on BASE, FOMO will be releasing the ability for future projects to be paired in $FOMO in the coming weeks. This is the biggest release we have ever announced. Launch your AI Agent Token + $FOMO trading pair Every individual agent token is paired with the $FOMO token in its liquidity pool. When launching an agent on you will need $FOMO tokens, which are used to create the liquidity pool. This process creates deflationary pressure for FOMO and the entire agent ecosystem. When creating your agent and token, you will have the option to pair your launch with FOMO or SOL, as our goal is not to alienate any project, but rather invite the best communities, CTO’s and builders to launch with us. If you decide to pair your project with FOMO you in turn get full marketing and dev support, once your project graduates the bonding curve and reaches Raydium. Further, as an added incentive, as our revenue grows we will be using part of the funds to support projects that have paired in FOMO. And Devs who launch tokens paired in FOMO will earn fees from their AI Agent token launch. Building the most robust agents using our framework will catapult us as one of the most prominent standards of the Solana ecosystem. Not only have we developed our own core infrastructure, but we also pull from some of the best repo’s and developer talent in all of AI, not just blockchain. Our team is comprised of 9 world class artificial intelligence engineers, PHDs in mathematics and engineering from the top companies on the cutting edge of AI. The future of AI Agents will be on Solana and we will help lead the way.

FOMO

129,867 просмотров • 1 год назад

the IBM earnings reinforced something I think Wall Street is still underestimating: IBM is becoming an operating system for enterprise AI. The initial earnings reaction focused on the familiar metrics—revenue growth, infrastructure, margins and quarterly expectations. But that misses the bigger transformation underway. The AI market is bifurcating. On one side are companies building frontier models. On the other are companies helping enterprises operationalize AI—integrating it with existing systems and data, governing it, securing it, and turning AI into measurable business outcomes. IBM is increasingly positioning itself for that second market. And that's important because the enterprise AI conversation is moving from models to tools, from experimentation to outcomes, and ultimately toward autonomy and agents. IBM has decades of enterprise infrastructure, software, data and consulting relationships sitting underneath that transition. The mainframe isn't necessarily legacy in this world—it can become part of the context and transaction infrastructure AI acts upon. Wall Street initially looked at the quarter. I think the more important question is what IBM is becoming. My commentary below. #IBM #AI #EnterpriseAI #AgenticAI #HybridCloud

the IBM earnings reinforced something I think Wall Street is still underestimating: IBM is becoming an operating system for enterprise AI. The initial earnings reaction focused on the familiar metrics—revenue growth, infrastructure, margins and quarterly expectations. But that misses the bigger transformation underway. The AI market is bifurcating. On one side are companies building frontier models. On the other are companies helping enterprises operationalize AI—integrating it with existing systems and data, governing it, securing it, and turning AI into measurable business outcomes. IBM is increasingly positioning itself for that second market. And that's important because the enterprise AI conversation is moving from models to tools, from experimentation to outcomes, and ultimately toward autonomy and agents. IBM has decades of enterprise infrastructure, software, data and consulting relationships sitting underneath that transition. The mainframe isn't necessarily legacy in this world—it can become part of the context and transaction infrastructure AI acts upon. Wall Street initially looked at the quarter. I think the more important question is what IBM is becoming. My commentary below. #IBM #AI #EnterpriseAI #AgenticAI #HybridCloud

John Furrier

47,614 просмотров • 7 дней назад

Spectre AI’s founder steps in front of the camera for Y Combinator . As part of our Y Combinator Summer Batch 2026 application, our founder Sunny Enzo recorded a founder video walking through the Spectre AI app and the direction of the company. Sunny is doxxed, but this is the first time he is appearing on video to speak directly about Spectre AI, the product, and the market intelligence layer we are building. Due to YC’s time format, the video only shows around 5 to 6 features from an ecosystem with 100+ features already built or in progress. A full Spectre AI walkthrough would need far more time. Still, it gives a clear look at where we are heading: AI market intelligence, on-chain data, social intelligence, real-time context, and a cleaner way to understand financial markets through one platform. Another step in taking Spectre AI from product delivery into serious company building.

Spectre AI’s founder steps in front of the camera for Y Combinator . As part of our Y Combinator Summer Batch 2026 application, our founder Sunny Enzo recorded a founder video walking through the Spectre AI app and the direction of the company. Sunny is doxxed, but this is the first time he is appearing on video to speak directly about Spectre AI, the product, and the market intelligence layer we are building. Due to YC’s time format, the video only shows around 5 to 6 features from an ecosystem with 100+ features already built or in progress. A full Spectre AI walkthrough would need far more time. Still, it gives a clear look at where we are heading: AI market intelligence, on-chain data, social intelligence, real-time context, and a cleaner way to understand financial markets through one platform. Another step in taking Spectre AI from product delivery into serious company building.

SPECTRE AI

17,501 просмотров • 2 месяцев назад

Last week $TEAM outlined a new AI bull case for SaaS, as a source of context to generate better, faster, cheaper AI outcomes. Atlassian demod how Claude Code by itself underperformed an identical instance of Claude Code, on an identical prompt, when the other instance had access to their Teamwork Graph (TWG) which has access to past project files, messages, documents, code bases, employee data, and system integrations (slack, outlook, etc). The added context pointed Claude to the right information to solve the task faster, whereas the instance without context thought more, read more, hallucinated, and got the task incorrect after spending 2x more tokens and running an extra minute. Since token spend is quickly becoming many multiples of traditional software cost for coding use cases (Jira at $10-12/seat vs Anthropic spend per seat in the several hundreds a month), Atlassian makes a compelling case that context pays for itself many times over by helping customers to get more value out of their expensive AI tokens. $MSFT easily has the strongest advantage as a source of differentiated context. Video worth watching.

Last week $TEAM outlined a new AI bull case for SaaS, as a source of context to generate better, faster, cheaper AI outcomes. Atlassian demod how Claude Code by itself underperformed an identical instance of Claude Code, on an identical prompt, when the other instance had access to their Teamwork Graph (TWG) which has access to past project files, messages, documents, code bases, employee data, and system integrations (slack, outlook, etc). The added context pointed Claude to the right information to solve the task faster, whereas the instance without context thought more, read more, hallucinated, and got the task incorrect after spending 2x more tokens and running an extra minute. Since token spend is quickly becoming many multiples of traditional software cost for coding use cases (Jira at $10-12/seat vs Anthropic spend per seat in the several hundreds a month), Atlassian makes a compelling case that context pays for itself many times over by helping customers to get more value out of their expensive AI tokens. $MSFT easily has the strongest advantage as a source of differentiated context. Video worth watching.

SouthernValue

31,196 просмотров • 2 месяцев назад

🩺🚀 SUI DeSci Agents is excited to introduce the #AI Medical Image Diagnosis Agent, an integral part of our #AI Longevity Assistant! This cutting-edge tool, within our wide product suite, is designed to onboard millions of longevity enthusiasts into the SUI $DeSci ecosystem while driving organic growth for the AI agents launched on our platform. 📊 How it works: - Upload X-rays, CT scans, MRIs, ultrasounds, etc. - Let #AI deliver precise analysis with actionable insights. Accurate medical imaging analysis is key to longevity — and we’re leading the charge. Data will be sold to longevity initiatives and research institutions with $DESCI token lying at the center of it all.

🩺🚀 SUI DeSci Agents is excited to introduce the #AI Medical Image Diagnosis Agent, an integral part of our #AI Longevity Assistant! This cutting-edge tool, within our wide product suite, is designed to onboard millions of longevity enthusiasts into the SUI $DeSci ecosystem while driving organic growth for the AI agents launched on our platform. 📊 How it works: - Upload X-rays, CT scans, MRIs, ultrasounds, etc. - Let #AI deliver precise analysis with actionable insights. Accurate medical imaging analysis is key to longevity — and we’re leading the charge. Data will be sold to longevity initiatives and research institutions with $DESCI token lying at the center of it all.

SUI Desci Agents

160,614 просмотров • 1 год назад

AI completely changes the way we can talk to data. Hex is leading the way by building collaborative, shareable analytics workspaces. I sat down with CEO Barry McCardel to discuss how agent workflows, conversational interfaces, and context-aware models are reshaping analysis. We cover how Hex aims to make everyone a data person by unifying analysis and AI in one workflow, how Hex is replacing outdated fragmented workflows, and the importance of trust and accuracy in AI systems. We also go over Hex’s awesome product launch videos, Barry’s emphasis on being fun and weird, and lessons he learned from Palantir. Enjoy! 0:00 The challenge of trustworthy data 1:19 Context management, observability, and governance 9:24 The promise and reality of data democratization 15:51 The impact of AI on data workflows 31:39 Breadth, depth, and speed: the three dimensions of AI in data 48:04 The rise of ambient analytics 55:56 The modern data stack and industry consolidation 1:03:40 Acquisitions and owning the data insight layer 1:06:06 Lessons from Palantir: forward-deployed engineering 1:13:13 Commitment engineering and customer collaboration 1:17:11 Brand, launch videos, and having fun in SaaS Barry McCardel Hex

Sarah Wang

41,746 просмотров • 7 месяцев назад

the trading terminal is almost ready and will go live next week. superior data, lower fees, AI-powered edge, token dashboards, live technical analysis, and much more. we have integrated our swarm of 7 domain-specific AI agents directly into the trading terminal to make streamlined analysis and ensure every single trencher has manual execution but can conduct any further analysis with our AI Agents. this changes the paradigm entirely; our trading terminal is built for everyone, however we're putting special emphasis on the newbies and less experienced traders, so they can be assisted by our agents and different capabilities integrated. e.g. you can conduct technical analysis on any token in seconds, on the time frame you have selected on the chart, to find signals, potential entries and recommendations on what to look for for confirmation. all tokens have agents assisting the trader, as well as informational dashboards comprising of social media & KOL analysis, trading metrics, macro news, general solana ecosystem knowledge, and security analysis. our fees are further reduced by holding our token: - base Fee: 0.8% - bronze (0.3% to 0.49% of $FRACTAL supply): 0.5% - silver (0.5% to 0.99% of $FRACTAL supply): 0.3% - gold (>1% of $FRACTAL supply): 0.1% btw, profitability has been achieved internally. ~ xFractal

the trading terminal is almost ready and will go live next week. superior data, lower fees, AI-powered edge, token dashboards, live technical analysis, and much more. we have integrated our swarm of 7 domain-specific AI agents directly into the trading terminal to make streamlined analysis and ensure every single trencher has manual execution but can conduct any further analysis with our AI Agents. this changes the paradigm entirely; our trading terminal is built for everyone, however we're putting special emphasis on the newbies and less experienced traders, so they can be assisted by our agents and different capabilities integrated. e.g. you can conduct technical analysis on any token in seconds, on the time frame you have selected on the chart, to find signals, potential entries and recommendations on what to look for for confirmation. all tokens have agents assisting the trader, as well as informational dashboards comprising of social media & KOL analysis, trading metrics, macro news, general solana ecosystem knowledge, and security analysis. our fees are further reduced by holding our token: - base Fee: 0.8% - bronze (0.3% to 0.49% of $FRACTAL supply): 0.5% - silver (0.5% to 0.99% of $FRACTAL supply): 0.3% - gold (>1% of $FRACTAL supply): 0.1% btw, profitability has been achieved internally. ~ xFractal

xFractal

33,047 просмотров • 9 месяцев назад

We've built 40+ AI agents and internal tools. The hardest part is Context Creation. AI runs playbooks and makes judgment calls for you. But without your company's context, you get slop. Context Creation means extracting the subject matter expertise and playbooks that live in people's heads, not in LLM training data, or even your tools. As forward deployed engineers (FDEs), we create context and turn it into code. We evaluate the business impact, how it aligns with the dev roadmap, and come up with creative solutions. We built The FDE Factory to replace ourselves. It drives AI adoption inside our clients' companies by running discovery sessions using prototypes to create context. Here's how it works: We put a prototype in front of a stakeholder. The stakeholder gives feedback via voice while they're using or reviewing it. Then our FDE Factory Agents builds in their expertise in minutes: > Context Agent reviews the codebase and feedback, extracts the requirements, and creates a spec > Scope Agent checks the spec against the development roadmap, validates it, and hands it off > Engineering Agent builds a new feature and wires the integration > QA Agent runs tests to prove to itself it works > PR merges, feature goes live, product updates itself in real time It's like the nontechnical stakeholder wrote the code without even knowing it. Coding agents are great at turning good development plans into code, and they're getting better at turning context into good development plans in collaboration with professional engineers. But nontechnical people are capped on what they can build without product people and engineers. The bridge that takes nontechnical people from vibe coding basic apps to building production AI tools that run on first party context is FDEs. Our new FDE Factory gives you the system to go from idea to production. Context Creation is the first and most important step in our FDE lifecycle, and we just automated it. Now clients get the right agents and tools built for them, customized to their unique business and encoded with their expertise. PS: If you're building AI agents within your company, reply "Playbook" and I'll DM you the entire FDE playbook we've run with 30+ companies. It covers finding high-impact AI use cases, building them, and deploying them across the org.

We've built 40+ AI agents and internal tools. The hardest part is Context Creation. AI runs playbooks and makes judgment calls for you. But without your company's context, you get slop. Context Creation means extracting the subject matter expertise and playbooks that live in people's heads, not in LLM training data, or even your tools. As forward deployed engineers (FDEs), we create context and turn it into code. We evaluate the business impact, how it aligns with the dev roadmap, and come up with creative solutions. We built The FDE Factory to replace ourselves. It drives AI adoption inside our clients' companies by running discovery sessions using prototypes to create context. Here's how it works: We put a prototype in front of a stakeholder. The stakeholder gives feedback via voice while they're using or reviewing it. Then our FDE Factory Agents builds in their expertise in minutes: > Context Agent reviews the codebase and feedback, extracts the requirements, and creates a spec > Scope Agent checks the spec against the development roadmap, validates it, and hands it off > Engineering Agent builds a new feature and wires the integration > QA Agent runs tests to prove to itself it works > PR merges, feature goes live, product updates itself in real time It's like the nontechnical stakeholder wrote the code without even knowing it. Coding agents are great at turning good development plans into code, and they're getting better at turning context into good development plans in collaboration with professional engineers. But nontechnical people are capped on what they can build without product people and engineers. The bridge that takes nontechnical people from vibe coding basic apps to building production AI tools that run on first party context is FDEs. Our new FDE Factory gives you the system to go from idea to production. Context Creation is the first and most important step in our FDE lifecycle, and we just automated it. Now clients get the right agents and tools built for them, customized to their unique business and encoded with their expertise. PS: If you're building AI agents within your company, reply "Playbook" and I'll DM you the entire FDE playbook we've run with 30+ companies. It covers finding high-impact AI use cases, building them, and deploying them across the org.

Mike Fishbein

10,137 просмотров • 1 месяц назад

Strategy is proud to announce a strategic partnership with marking an important milestone in their AI journey. Strategy makes AI reliable on enterprise data, and is committed to helping organizations transform data into actionable insights and make faster, more informed decisions. Through this collaboration, we are enabling to further leverage data as a strategic asset across their organization, powered by Strategy Mosaic, our Universal Semantic Layer that gives AI the business context it needs to produce consistent, governed results. This partnership aligns with long-term vision of embedding AI and data-driven decision-making into every aspect of their business. By enhancing their analytical capabilities with Strategy, we are helping to build a stronger foundation for innovation, operational excellence, and customer value. As continues its AI-first journey, Strategy is proud to play a role in supporting their growth and shaping the future of their organization. #AI #SemanticLayer #InsuranceTech #StrategyMosaic

Strategy is proud to announce a strategic partnership with marking an important milestone in their AI journey. Strategy makes AI reliable on enterprise data, and is committed to helping organizations transform data into actionable insights and make faster, more informed decisions. Through this collaboration, we are enabling to further leverage data as a strategic asset across their organization, powered by Strategy Mosaic, our Universal Semantic Layer that gives AI the business context it needs to produce consistent, governed results. This partnership aligns with long-term vision of embedding AI and data-driven decision-making into every aspect of their business. By enhancing their analytical capabilities with Strategy, we are helping to build a stronger foundation for innovation, operational excellence, and customer value. As continues its AI-first journey, Strategy is proud to play a role in supporting their growth and shaping the future of their organization. #AI #SemanticLayer #InsuranceTech #StrategyMosaic

Strategy

55,035 просмотров • 18 дней назад

Head of Product at Claude on how Anthropic's sales team is integrating external data into their AI to create custom sales decks and land potential customers People are still missing the fact that the internal teams inside these LLM companies have access to frontier models and dont have the same token constraints that rest of us do That within itself is a competitive advantage, and will create a loop where foundational AI labs grow their business at a much faster and efficient rate than everyone else From Cat Wu's conversation with Lenny

Head of Product at Claude on how Anthropic's sales team is integrating external data into their AI to create custom sales decks and land potential customers People are still missing the fact that the internal teams inside these LLM companies have access to frontier models and dont have the same token constraints that rest of us do That within itself is a competitive advantage, and will create a loop where foundational AI labs grow their business at a much faster and efficient rate than everyone else From Cat Wu's conversation with Lenny

Boring_Business

75,612 просмотров • 3 месяцев назад

I sat down with Manish Gupta, co-founder of Corridor Platforms, to dive into how AI (and Gen AI) is reshaping the highly regulated banking industry. From automating customer interactions to tackling risk & compliance, banks have to adapt—or fall behind. How do you govern AI in a world where trust is everything? Manish breaks it down, including: ✅ AI-driven customer service that actually works ✅ The challenge of bias & compliance in AI decisions ✅ Why smaller banks might have an edge over the big guys ✅ The secret to getting AI past the first sales call in enterprise The race is on. Banks that embrace AI responsibly will dominate the next decade. 🚀 Watch the full conversation here:

I sat down with Manish Gupta, co-founder of Corridor Platforms, to dive into how AI (and Gen AI) is reshaping the highly regulated banking industry. From automating customer interactions to tackling risk & compliance, banks have to adapt—or fall behind. How do you govern AI in a world where trust is everything? Manish breaks it down, including: ✅ AI-driven customer service that actually works ✅ The challenge of bias & compliance in AI decisions ✅ Why smaller banks might have an edge over the big guys ✅ The secret to getting AI past the first sales call in enterprise The race is on. Banks that embrace AI responsibly will dominate the next decade. 🚀 Watch the full conversation here:

Robert Scoble

30,917 просмотров • 1 год назад

We talked to Micky 🐸, the founder of Ribbit Capital and early backer of Coinbase, Robinhood, and Bridge, about what’s coming next. "I’m more excited for the next decade than the last." “Everything is a token. We call it the token factory revolution; tokenizing information, money, and power.” AI, payments, and financial services are converging fast, and tokens are becoming the raw material for decision-making, coordination, and access. Micky sees a new stack forming where every company is either building, supplying, or orchestrating tokens. And he bets on a generation of young founders who are native to this shift: “Their brains are just faster. The best ones are already building access tokens, expert tokens, asset tokens… stablecoins were only the first chapter.” From his appearance on the show in July.

We talked to Micky 🐸, the founder of Ribbit Capital and early backer of Coinbase, Robinhood, and Bridge, about what’s coming next. "I’m more excited for the next decade than the last." “Everything is a token. We call it the token factory revolution; tokenizing information, money, and power.” AI, payments, and financial services are converging fast, and tokens are becoming the raw material for decision-making, coordination, and access. Micky sees a new stack forming where every company is either building, supplying, or orchestrating tokens. And he bets on a generation of young founders who are native to this shift: “Their brains are just faster. The best ones are already building access tokens, expert tokens, asset tokens… stablecoins were only the first chapter.” From his appearance on the show in July.

TBPN

106,752 просмотров • 8 месяцев назад

AI spend is now one of the fastest-growing line items at every company, but here's the problem: it's invisible to traditional spend management. We experienced this firsthand at Brex, so we built Magpie: an internal tool that sources and normalizes AI spend across every vendor so we can break it down by model, customer, employee, and workflow. What Magpie has already unlocked: → ~85% cost reduction on an audit agent through prompt caching → Real customer cost-to-serve numbers that inform pricing and margin → Model routing governance we couldn't enforce before → A surprise: operational AI is much cheaper than we assumed So why is AI spend so hard to track? Because each one exposes usage data differently (some not at all). At Brex, tool-use costs weren't tracked alongside tokens, customer attribution was inconsistent, and a meaningful chunk of our daily AI spend was unattributable. Now with Magpie, we have reliable visibility. AI spend is more complex than anything finance teams have had to manage before, and it's exactly the kind of problem Brex is built to solve, shaping how we think about the future of our platform. More below.

AI spend is now one of the fastest-growing line items at every company, but here's the problem: it's invisible to traditional spend management. We experienced this firsthand at Brex, so we built Magpie: an internal tool that sources and normalizes AI spend across every vendor so we can break it down by model, customer, employee, and workflow. What Magpie has already unlocked: → ~85% cost reduction on an audit agent through prompt caching → Real customer cost-to-serve numbers that inform pricing and margin → Model routing governance we couldn't enforce before → A surprise: operational AI is much cheaper than we assumed So why is AI spend so hard to track? Because each one exposes usage data differently (some not at all). At Brex, tool-use costs weren't tracked alongside tokens, customer attribution was inconsistent, and a meaningful chunk of our daily AI spend was unattributable. Now with Magpie, we have reliable visibility. AI spend is more complex than anything finance teams have had to manage before, and it's exactly the kind of problem Brex is built to solve, shaping how we think about the future of our platform. More below.

Pedro Franceschi

28,492 просмотров • 2 месяцев назад

Most discussions around Claude Code focus on model quality or capability. But when you actually use it, the friction shows up earlier, more at the system level than the model itself. Token limits get the blame, but a lot of it comes from inefficient sessions: repeated context, extra tool calls, and small loops that quietly stack up. So it starts to feel less like “is the model good enough?” and more like “is the workflow slowing it down?” That’s where WozCode comes in. It helps streamline sessions, less repetition, smarter batching of tool calls, and tighter context usage so you can actually get more work done within the same limits. Woz just launched on Product Hunt. If Claude Code felt a bit restrictive before, it’s worth trying again with a cleaner workflow setup. Same model. Smoother execution. Upvote here:

Most discussions around Claude Code focus on model quality or capability. But when you actually use it, the friction shows up earlier, more at the system level than the model itself. Token limits get the blame, but a lot of it comes from inefficient sessions: repeated context, extra tool calls, and small loops that quietly stack up. So it starts to feel less like “is the model good enough?” and more like “is the workflow slowing it down?” That’s where WozCode comes in. It helps streamline sessions, less repetition, smarter batching of tool calls, and tighter context usage so you can actually get more work done within the same limits. Woz just launched on Product Hunt. If Claude Code felt a bit restrictive before, it’s worth trying again with a cleaner workflow setup. Same model. Smoother execution. Upvote here:

Parul Gautam

15,079 просмотров • 2 месяцев назад