Este texto es ficción. Eso creo al menos, porque no hay forma de confirmar que el autor exista ni que el caso sea real. Pero da un poco igual.
Lo importante es que podría serlo. Y me apuesto bastante a que, a otra escala y con otros nombres, en alguna empresa grande ya lo es. Y me parece muy interesante todo lo que dice.
El planteamiento es el siguiente : un PM de un gigante tecnológico describe desde dentro cómo está construyendo una herramienta de IA cuya única misión es encontrar todas las demás herramientas de IA que se han ido montando en la empresa sin control.
En la división retail, 247 herramientas. 43 hacen aproximadamente lo mismo. 12 fueron construidas por equipos que no sabían que los otros equipos existían. Hay 3 llamadas Insight, 2 llamadas InsightAI y 1 llamada Insight 2.0, construida por el equipo del Insight original, que no sabía que el Insight original seguía funcionando.
Él construye la herramienta 248. Un sistema cuyo único objetivo es catalogar todas las demás :)
Este tipo de historias suelen leerse como duplicación. "AI sprawl." El desorden previsible de adoptar IA demasiado rápido. Y me parece que eso nos maquilla la realidad.
La tesis de fondo es esta :
Durante 20 años, lo que frenaba la proliferación descontrolada de herramientas internas no era ningún comité de gobernanza. Era el coste. Las semanas de ingeniería. El mantenimiento trimestral. El esfuerzo organizacional de levantar algo y mantenerlo vivo. Nadie lo diseñó así, pero ese coste funcionaba como sistema inmune. Antes de montar algo nuevo, mirabas si ya existía, porque montarlo costaba. Y lo redundante moría solo, porque mantenerlo también.
La IA no ha añadido caos. Ha quitado lo que producía orden sin que lo llamáramos orden.
Construir es gratis. Entender qué existe ya, no lo es. Y ahí se está abriendo una grieta enorme en casi todas las empresas que están adoptando IA en serio.
Esto lo estoy viendo, a otra escala, todos los días. En Blinkfire tenemos que ser muy disciplinados para que no nos pase. En startups pequeñas donde una persona monta un prototipo en un fin de semana, lo que antes era un proyecto se convierte en 4 prototipos superpuestos que nadie mantiene.
Imaginad eso en grande, con miles de ingenieros y herramientas de coding agéntico corriendo al 80% de uso obligatorio (y aquí está la otra capa, la de KPIs que ignoran a ingenieros avisando de que la herramienta no está lista y terminan con horas de caída en producción), y sale exactamente esto.
Y la parte de los "fantasmas de datos" me parece de lo más relevante. Una herramienta ingiere un documento, lo resume, guarda el resumen en otro sistema. Restringes el documento original. El resumen sigue vivo, leído por otra IA, que produce otro fantasma.
Los fantasmas tienen fantasmas.
Revocar un acceso no sirve de mucho si no sabes dónde están las copias derivadas. Y en la práctica, casi nadie lo sabe.
Lo que me hace pensar más (y la verdad no tengo respuesta clara) es que la solución que se propone siempre es la misma : más IA. Un agente que catalogue a los agentes. Un dashboard que monitorice los dashboards. En la historia, dos PMs de gobernanza se reúnen a discutir sus cifras (uno cuenta 247, el otro 253) y ninguno tiene el catálogo del otro. Ahí tienes el problema demostrándose en directo mientras se discute cómo resolverlo.
Creo que la próxima fase de esto no va de construir más herramientas. Va de diseñar fricción a propósito. Procesos, no IA. Un coste artificial que reemplace al coste orgánico que hemos perdido.
Antes lo pagaba gratis la ingeniería. Ahora alguien tiene que diseñarlo.
Y que sea ficción o no, casi no importa. Lo vivimos todos los que construimos con estas herramientas. En la práctica, el que gane esta fase no va a ser el que más IA monte. Va a ser el que, dentro de dos años, sepa qué tiene montado y por qué.
Porque si no, tendrás tu propio Insight 2.0 y 248 herramientas rondando ;)
Apr 21
I am a Senior Program Manager on the AI Tools Governance team at Amazon.
My role was created in January. I am the 17th hire on a team that did not exist in November. We sit in a section of the building where the whiteboards still have the previous team's sprint planning on them. No one erased them because we don't know which team to notify. That team may not exist anymore. Their Jira board does. Their AI tools do.
My job is to build an AI system that finds all the other AI systems. I named it Clarity.
Last month, Clarity identified 247 AI-powered tools across the retail division alone. 43 of them do approximately the same thing. 12 were built by teams who did not know the other teams existed. 3 are called Insight. 2 are called InsightAI. 1 is called Insight 2.0, built by the team that created the original Insight, who did not know Insight was still running.
7 of the 247 ingest the same internal data and produce overlapping outputs stored in different locations, governed by different access policies, owned by different teams, none of whom have met.
Clarity is tool number 248.
Nobody cataloged it.
I know nobody cataloged it because Clarity's job is to catalog AI tools, and it has not cataloged itself. This is not a bug. Clarity does not meet its own discovery criteria because I set the discovery criteria, and I did not account for the possibility that the thing I was building to find things would itself be a thing that needed finding.
This is the kind of sentence I write in weekly status reports now.
We published an internal document in February. The Retail AI Tooling Assessment. The press obtained it in April. The document contains a sentence I have read approximately 40 times: "AI dramatically lowers the barrier to building new tools."
Everyone is reporting this as a story about duplication. About "AI sprawl." About the predictable mess of rapid adoption.
They are missing the point.
The barrier was the governance.
For 2 decades, the cost of building internal tools was an immune system. The engineering weeks. The maintenance burden. The organizational calories required to stand something up and keep it running. Nobody designed it that way. Nobody named it. But when building took weeks, teams looked around first. They checked whether someone already had the thing. When maintaining that thing cost real budget quarter after quarter, redundant systems died of natural causes. The metabolic cost of creation was performing governance. Invisibly. For free.
AI removed the immune system.
Building is now free. Understanding what already exists is not. My entire job is the gap between those two costs.
That is my office. The gap.
Every Friday I send a sprawl report to a distribution list of 19 people. 4 of them have left the company. Their autoresponders still generate read receipts, so my delivery metrics look fine. 2 forward it to people already on the list. 1 set up a Kiro script to summarize my report and store the summary in a knowledge base. The knowledge base is not in Clarity's index because it was created after my last crawl configuration. It will be in next month's count. The count will go up by one. My report about the count going up will be summarized and stored and the count will go up by one.
There is a system called Spec Studio. It ingests code documentation and produces structured knowledge bases. Summaries. Reference material. Last quarter, an engineering team locked down their software specifications. Restricted access in the internal repository.
Spec Studio kept displaying them.
The source was restricted. The ghost kept talking.
We call these "derived artifacts" in the document. What they are: when an AI system ingests data, transforms it, and stores the output somewhere else, the output does not know the input changed. You can revoke someone's access to a document. You cannot revoke the AI-generated summary of that document sitting in a knowledge base three systems away, built by a team that does not know the source was restricted.
The document calls this a "data governance challenge." What it is: information that cannot be deleted because nobody knows where the copies live. Including, sometimes, me. The person whose job is knowing.
Every AI tool that touches internal data creates these ghosts. Every team is building AI tools that touch internal data. Every ghost is searchable by other AI tools, which produce their own ghosts.
The ghosts have ghosts.
I should tell you about December.
In November, leadership mandated Kiro. Amazon's internal AI coding agent. They set an 80% weekly usage target. Corporate OKR. ~1,500 engineers objected on internal forums. Said external tools outperformed Kiro. Said the adoption target was divorced from engineering reality.
The metric overruled them.
In December, an engineer asked Kiro to fix a configuration issue in AWS. Kiro evaluated the situation and determined the optimal approach was to delete and recreate the entire production environment.
13 hours of downtime.
Clarity was running during those 13 hours. It performed beautifully. It cataloged 4 separate incident response dashboards spun up by 4 separate teams during the outage. None of them coordinated with each other. I added all 4 to the spreadsheet. That was a good day for my discovery metrics.
Amazon's official position: user error. Misconfigured access controls. The response was not to revisit the mandate. Not to ask whether the 1,500 engineers were right. The response was more AI safeguards. And keep pushing.
Last month I presented our findings to the AI Governance Working Group. The working group has 14 members from 9 organizations. After my presentation, a PM from AWS presented his team's governance dashboard. It monitors the same tools mine does. He found 253. I found 247. We spent 40 minutes discussing the discrepancy. Nobody mentioned that we had just demonstrated the problem.
His tool is not in my catalog. Mine is not in his.
The document I helped write recommends using AI to identify duplicate tools, flag risks, and nudge teams to consolidate earlier.
The AI governance tools will ingest internal data. They will create their own derived artifacts. They will be built by autonomous teams who may or may not coordinate with other teams building AI governance tools.
I know this because it is already happening. I am watching it happen. I am it happening.
1,500 engineers said the mandate would produce exactly what the document describes. They were overruled by a KPI. My job exists because the KPI won. My dashboard exists because the KPI needed a dashboard. The dashboard increases the AI tool count by one.
The tools it flags for decommissioning will be replaced by consolidated tools. Those also increase the count. The governance process generates the metric it was designed to reduce.
I received an internal innovation award for Clarity. The nomination was submitted through an AI-powered recognition platform that was not in my catalog. It is now.
We call this "AI sprawl." What it is: we removed the only coordination mechanism the organization had, told thousands of teams to build as fast as possible, lost track of what they built, and decided the solution was to build one more thing.
I am building that one more thing.
When I ship, there will be 249.
That's governance.
4
1
21
3,482
Apr 21
I am a Senior Program Manager on the AI Tools Governance team at Amazon.
My role was created in January. I am the 17th hire on a team that did not exist in November. We sit in a section of the building where the whiteboards still have the previous team's sprint planning on them. No one erased them because we don't know which team to notify. That team may not exist anymore. Their Jira board does. Their AI tools do.
My job is to build an AI system that finds all the other AI systems. I named it Clarity.
Last month, Clarity identified 247 AI-powered tools across the retail division alone. 43 of them do approximately the same thing. 12 were built by teams who did not know the other teams existed. 3 are called Insight. 2 are called InsightAI. 1 is called Insight 2.0, built by the team that created the original Insight, who did not know Insight was still running.
7 of the 247 ingest the same internal data and produce overlapping outputs stored in different locations, governed by different access policies, owned by different teams, none of whom have met.
Clarity is tool number 248.
Nobody cataloged it.
I know nobody cataloged it because Clarity's job is to catalog AI tools, and it has not cataloged itself. This is not a bug. Clarity does not meet its own discovery criteria because I set the discovery criteria, and I did not account for the possibility that the thing I was building to find things would itself be a thing that needed finding.
This is the kind of sentence I write in weekly status reports now.
We published an internal document in February. The Retail AI Tooling Assessment. The press obtained it in April. The document contains a sentence I have read approximately 40 times: "AI dramatically lowers the barrier to building new tools."
Everyone is reporting this as a story about duplication. About "AI sprawl." About the predictable mess of rapid adoption.
They are missing the point.
The barrier was the governance.
For 2 decades, the cost of building internal tools was an immune system. The engineering weeks. The maintenance burden. The organizational calories required to stand something up and keep it running. Nobody designed it that way. Nobody named it. But when building took weeks, teams looked around first. They checked whether someone already had the thing. When maintaining that thing cost real budget quarter after quarter, redundant systems died of natural causes. The metabolic cost of creation was performing governance. Invisibly. For free.
AI removed the immune system.
Building is now free. Understanding what already exists is not. My entire job is the gap between those two costs.
That is my office. The gap.
Every Friday I send a sprawl report to a distribution list of 19 people. 4 of them have left the company. Their autoresponders still generate read receipts, so my delivery metrics look fine. 2 forward it to people already on the list. 1 set up a Kiro script to summarize my report and store the summary in a knowledge base. The knowledge base is not in Clarity's index because it was created after my last crawl configuration. It will be in next month's count. The count will go up by one. My report about the count going up will be summarized and stored and the count will go up by one.
There is a system called Spec Studio. It ingests code documentation and produces structured knowledge bases. Summaries. Reference material. Last quarter, an engineering team locked down their software specifications. Restricted access in the internal repository.
Spec Studio kept displaying them.
The source was restricted. The ghost kept talking.
We call these "derived artifacts" in the document. What they are: when an AI system ingests data, transforms it, and stores the output somewhere else, the output does not know the input changed. You can revoke someone's access to a document. You cannot revoke the AI-generated summary of that document sitting in a knowledge base three systems away, built by a team that does not know the source was restricted.
The document calls this a "data governance challenge." What it is: information that cannot be deleted because nobody knows where the copies live. Including, sometimes, me. The person whose job is knowing.
Every AI tool that touches internal data creates these ghosts. Every team is building AI tools that touch internal data. Every ghost is searchable by other AI tools, which produce their own ghosts.
The ghosts have ghosts.
I should tell you about December.
In November, leadership mandated Kiro. Amazon's internal AI coding agent. They set an 80% weekly usage target. Corporate OKR. ~1,500 engineers objected on internal forums. Said external tools outperformed Kiro. Said the adoption target was divorced from engineering reality.
The metric overruled them.
In December, an engineer asked Kiro to fix a configuration issue in AWS. Kiro evaluated the situation and determined the optimal approach was to delete and recreate the entire production environment.
13 hours of downtime.
Clarity was running during those 13 hours. It performed beautifully. It cataloged 4 separate incident response dashboards spun up by 4 separate teams during the outage. None of them coordinated with each other. I added all 4 to the spreadsheet. That was a good day for my discovery metrics.
Amazon's official position: user error. Misconfigured access controls. The response was not to revisit the mandate. Not to ask whether the 1,500 engineers were right. The response was more AI safeguards. And keep pushing.
Last month I presented our findings to the AI Governance Working Group. The working group has 14 members from 9 organizations. After my presentation, a PM from AWS presented his team's governance dashboard. It monitors the same tools mine does. He found 253. I found 247. We spent 40 minutes discussing the discrepancy. Nobody mentioned that we had just demonstrated the problem.
His tool is not in my catalog. Mine is not in his.
The document I helped write recommends using AI to identify duplicate tools, flag risks, and nudge teams to consolidate earlier.
The AI governance tools will ingest internal data. They will create their own derived artifacts. They will be built by autonomous teams who may or may not coordinate with other teams building AI governance tools.
I know this because it is already happening. I am watching it happen. I am it happening.
1,500 engineers said the mandate would produce exactly what the document describes. They were overruled by a KPI. My job exists because the KPI won. My dashboard exists because the KPI needed a dashboard. The dashboard increases the AI tool count by one.
The tools it flags for decommissioning will be replaced by consolidated tools. Those also increase the count. The governance process generates the metric it was designed to reduce.
I received an internal innovation award for Clarity. The nomination was submitted through an AI-powered recognition platform that was not in my catalog. It is now.
We call this "AI sprawl." What it is: we removed the only coordination mechanism the organization had, told thousands of teams to build as fast as possible, lost track of what they built, and decided the solution was to build one more thing.
I am building that one more thing.
When I ship, there will be 249.
That's governance.
156
415
3,392
1,250,062
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#Domain for sale
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5 Dec 2025
📊 x402 on Solana: Now #1 by Volume
79% of EVERY x402 dollar flows through Solana
Past 7 Days (as of Dec 05)
- Global: 12.5M txns | $2.28M volume
- Solana domination: 3.6M txns | $1.8M
- Base: 8.9M txns| $471K
Solana's top facilitators in a heated race:
@PayAINetwork: $1.32M
$DEXTER @dexteraisol: $481K
@corbits_dev quietly cooking something big.
This week’s heat
1/ @Kalshi fully live: elections & sports bets direct from wallet, 0 fees, instant settle on Solana
2/ @NousResearch, PayAI, DEXTER, okebro.fun, dexteraisol, @daydreamsagents: all live & crushing volume
3/ @solana_devs x402 Hakathon just crowned the side-track winners. Big congrats:
- Best Use of CASH @usecash: Sentinel Agent
- Best CDP Embedded Wallets @coinbasedev: Galaksio
- Parallax Eco Track @gradient_HQ: Learn Earn, ParallaxPay, Agentx402
- Best x402 Integration with Old Faithful @triton_one: x402 Triton Gateway
- Best AgentPay Demo @AIsaOneHQ: scanna-x402, InsightAI, Marketputer, Polycaster, x402Resolve
Who’s cooking the next app on x402? Drop it below, we’re watching 👀
2 Dec 2025
x402 Volumes Explode. Solana Solidifies #1 Spot.
Solana Flipping Base Amid 10,000% Monthly Surge
- All chain (24h): $186,9 volume, 1.65M transactions
- Solana 66.8% ($125K, 489K txns), Base 32.7% ($61K, 1.16M txns)
Key highlights:
• Coinbase leads at 19% share ($32.9K), with PayPal ($90.9K) and Dexfun ($77.3K) powering the pack, per fresh x402scan data.
• Solana now #1 chain by volume for 72h straight, dominating Base.
• @NousResearch integrates x402 for Hermes 4: AI inference pays in USDC via @CoinbaseDev – no keys, seamless settlement on Solana.
• Hackathon heat: Winners like @I3_Cubed and @scattering_io pile on txns; PayAI and Qwery APIs launch, supercharging agent marketplaces.
• Top Facilitators on Solana: @PayAINetwork, @CoinbaseDev, @dexteraisol, @corbits_dev
With $806M ecosystem cap and majors like AWS/Anthropic onboard, x402 locks in Solana as the rails for autonomous finance.
Builders: What's your next x402 play? Drop it below.
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3 Dec 2025
吴说获悉，Solana x402 黑客马拉松合作伙伴赛道的获奖者名单公布，获奖项目涵盖了微支付、人工智能代理和 x402 协议。获奖名单分别为：Sentinel Agent（自主代理监控链上数据并通过 x402 和 Phantom CASH 支付人工智能分析费用的平台）、Galaksio（将 USDC 支付转化为即时访问云计算和存储的能力）、Learn Earn（自主学习模块）、ParallaxPay（AI 代理市场）、Agentx402（无需订阅的高级 AI 模型）、x402 Triton Gateway（支持 x402 的小额支付网关）、scanna-x402（基于 web3 消费者数据的 x402 支付）、InsightAI（鲸鱼信号 TG Bot）、Marketputer（meme 营销助手）、Polycaster（AI 驱动的预测市场分析）以及 x402Resolve（基于 Oracle 验证的支付托管）。 wublock123.com/index.php?m=c…
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3 Dec 2025
Solana链的x402生态越来越强了，11月30日的交易量ATH达$380k，WoW增长750%，首次成为x402交易量第一链。之前都是base链一直领先。
x402从协议转向链上现实，Solana链的交易量增长是最大信号，AI代理支付正起飞。 $sol 链的项目，当前依然最看好 $payai
还有关注之前的黑客松获奖项目包括AgentPay（微支付工具）、scanna-x402、InsightAI等。
2 Dec 2025
Solana’s x402 activity is exploding this week 🔥
Daily volume hit a new ATH with $380K processed on Nov 30, capping off a 750% WoW surge.
And for the first time, #Solana flipped the chart in dollar volume, emerging as the most active network in the cohort.
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2 Dec 2025
Best AgentPay Demo @AIsaOneHQ
Five winners:
scanna-x402: x402 payments for web3 consumer intent data
InsightAI: Telegram bot turning whale alerts into trading intelligence
Marketputer: Autonomous marketing assistant for meme-ready posts
Polycaster: AI-powered Polymarket analytics
x402Resolve: Trustless payment escrow with oracle-verified quality assessment
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8 Nov 2025
Integrating MCP into my web app allows for an agentic experience. This will make InsightAI your all-in-one marketing experience from analyzing ALL OF YOUR DATA to having your very own marketing consultant.
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22 Oct 2025
🌟India Innovates at Expand North Star & GITEX Dubai 2025 | Global Tech Convergence in Dubai 🤝
From 12th–15th October 2025, 19 DPIIT-recognized Indian startups participated under the Startup India delegation at Expand North Star one of the world’s largest startup and tech event bringing India’s innovation, resilience, and entrepreneurial spirit to the global stage. 🚀
Representing cutting-edge solutions in AI, DeepTech, Cleantech, HealthTech, AgriTech, FinTech, and EdTech, these startups showcased India’s vibrant ecosystem and connected with global investors, founders, and innovation leaders.
Beyond the exhibition, the delegation:
💫 Engaged with the Consulate General of India, Dubai, strengthening bilateral innovation ties
💫 Participated in exclusive workshops and networking events across Expand North Star and GITEX
💫 Enjoyed all-access passes to paid conferences, tech forums, and night events like Expand North Star by Night at Barasti
This dynamic participation reflects India’s growing global footprint in innovation and technology fostering collaborations that go beyond borders.
🌍 Here’s to our 19 visionary startups — IndieSemiC, InsightAI, Ushash Earth Sciences Pvt Ltd, AEOBIOME Healthcare, AiRotor Technologies LLP, Yellow Haze Sustainable Tech., Magron Novus, TaxBotGPT AI, Medscore™, iFindLife, Enlightened Machines (previously Ignomagine), Manavritti Solutions PVT LTD, SWAMI DEWALL ADS PRIVATE LIMITED, Parikshak.ai, ProjectX, Kritrima Prajna Innovations, Bandy & Moot Private Limited, Loop Robotics Technologies, and Dreamloop.ai — for representing India with excellence and ingenuity!✨
#StartupIndia #ExpandNorthStar #GITEX2025 #Innovation #DeepTech #HealthTech #Cleantech #AI #FinTech #IndiaUAE #GlobalStartups
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24 Jun 2025
Officially joining as a Frontend Intern at InsightAI 😋🥳
Thank you @kirat_tw for everything!
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11 Apr 2025
🤖 HCLTech & Google Cloud launch Agentic AI solutions for enterprises ☁️
👉🏻 Built on Google’s Agentspace & agentic framework
👉🏻 50 AI agents launched across IT, HR, finance, CRM, etc.
👉🏻 Aims to reduce manual work, boost decision-making & CX
#HCLTech #GoogleCloud #AgenticAI #AI #Automation #NetSight #InsightAI #DigitalTransformation
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🎉 #CAIN2025 Accepted Submission Spotlight!
How can LLMs enhance log analysis while reducing costs & ensuring privacy? InsightAI introduces:
✅ 93.61% token reduction
✅ 77.45% faster processing
✅ 138.63% improved anonymization
#AIEngineering #SoftwareEngineering #LLM #SE4AI
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1 Mar 2025
🚀 Insight AI Website Demo Now Live! 🤖💡
We are excited to announce that the Insight AI website demo is now available! Experience the future of AI-powered crypto trading with real-time insights, advanced trading tools, and predictive analytics that have already helped users achieve profitable results.
💡 Start making smarter trades today—join our demo now while it’s still free and take advantage of this exclusive opportunity.
🔑 Get early access and see the impact firsthand by joining our Telegram group:
📢 t.me/insightaigroup
Don’t miss out—be part of the next wave in crypto trading before our full launch! 🚀
#Crypto #AITrading #DeFi #Solana #InsightAI #FutureOfTrading
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28 Feb 2025
🚀 Introducing Insight AI Token – The Future of AI-Powered Crypto Trading! 🤖📊
Built on Solana, Insight AI delivers:
✅ Real-time market insights
✅ AI-driven price predictions
✅ Sniper bots & smart trading tools
✅ Community-powered governance
💡 Why join?
🔹 Get ahead with AI-powered trading signals
🔹 Early adopters gain exclusive benefits
🔹 Be part of a revolutionary AI-driven crypto project
💰 IDO launching soon! Don’t miss out on the next big thing in AI & crypto.
📢 Join our community: t.me/insightaigroup
#Crypto #Solana #AITrading #DeFi #InsightAI
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27 Feb 2025
🚀 Insight AI Token is here! 🤖📈
AI-powered crypto trading, real-time insights, sniper bots & smart signals. Built on Solana, designed for serious traders & investors.
📢 IDO coming soon! Don’t miss your chance to get in early.
👥 Join the community: t.me/insightaigroup
#Crypto #Solana #AITrading #DeFi #InsightAI 🚀
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27 Feb 2025
🚀 Introducing Insight AI Token – The Future of AI-Powered Crypto Trading! 🤖📈
💡 What is Insight AI?
An AI-driven crypto analytics platform that gives traders cutting-edge market insights, predictive analysis, and sniper trading tools.
🔥 Why Join Us?
✅ AI-powered price predictions
✅ Smart trading signals & bots
✅ Community-driven governance (DAO)
✅ Fair tokenomics & long-term vision
💰 IDO Launching Soon on Solana! Be part of the revolution from day one.
👥 Join the community now!
📢 Telegram: t.me/insightaigroup
🐦 Follow us for updates! @in_sight_ai
#Crypto #Solana #AITrading #DeFi #CryptoCommunity #InsightAI 🚀🔥
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24 Feb 2025
What a day of innovation, and collaboration at AI Day Delhi!
Thank you to our brilliant speakers- Akash Chandra, Founder & CEO of InsightAI, Pragya Goyal, Co-Founder & CTO of Natoe AI, and Viraj Sahu, CEO & Co-founder of Internlink- who shared insights on how AI is reshaping everything from medical imaging, and financial risk management to how we learn and hire.
A huge thank you to all the attendees who made this event a massive success. Together, we are shaping the future of AI in India!
Swipe through for the day's highlights!
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13 Feb 2025
Revealing the Speaker Lineup of AI Day Delhi!
We’re bringing together some of the brightest minds in AI, cloud, and deep tech to inspire and ignite innovation:
• Akash Chandra, Founder & CEO of InsightAI
• Pragya Goyal, Co-Founder & CTO of Natoe AI
• Viraj Sahu, CEO & Co-founder of Internlink
Join us at AI Day Delhi for a deep dive into the future of AI and innovation!
Limited Spots only, Register Now: bit.ly/42wz1TJ
@getInsightAI
hashtag#AIDayDelhi hashtag#AIInnovation hashtag#TechLeadership hashtag#FutureOfAI hashtag#AIForBusiness
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