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Meesho never built a chatbot. Not once. Vidit explains why on Signal Dialogues: chat isn’t native to how India’s next 500M users shop, so Meesho bet on voice instead a harder problem, but the right one. The results so far: 75% of orders now come from AI recommendations, not... show more

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1,463 subscribers

11,251 views • 2 months ago •via X (Twitter)

Anya Rossi• Live Now

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10 Comments

Ricky2 months ago

But that voice is similar to boys saying the same statment like recorder

sahil salve2 months ago

@Meesho_Official नमस्का l,में साहिल महाराष्ट्र के बीड़ जिले का निवासी हु मैने meesho से किताब ऑर्डर किया था अंग्रेजी मीडियम में और मुझे मिला हिन्दी मीडियम में meesho order id 313868111111455425 Abhi meri pariksha आनेवाली hai

Rupali Agrawal2 months ago

@Meesho_Official @Meesho_Official Return marked cancelled at 10:07 AM on system before executive Suraj Choudhary arrived at 11:30 AM & marked false QC fail! Agent Geetanjali force closed chat without help. Pls check DM & arrange re-pickup & refund!

FounderKraft2 months ago

"Built a local-first profit margin and RTO calculator for Indian marketplaces (Amazon, Flipkart, Zepto). Would love feedback on the UI/UX." No signup needed 👇

Rupali Agrawal2 months ago

@Meesho_Official @Meesho_Official @Delhivery False updates by executive Suraj Choudhary! AWB 1490711171879584 was marked cancelled on system at 10:08 AM before pickup visit! Order #314666845689599488 & #314730527168856768. Agent force closed chat. Need re-pickup & refund!

Activate2 months ago

Watch the full conversation here:

Rupali Agrawal2 months ago

@Meesho_Official My return was falsely marked cancelled/QC failed at 10:07 AM before executive Mr. Suraj Choudhary arrived at 11:30 AM. Chat was also force-closed. Kindly initiate re-pickup & refund! @Meesho_Official @Delhivery @delhiveryhelp #Meesho #Delhivery #CustomerService #ConsumerRights

Amit Jain2 months ago

Mera kya kara sir aapne

Khushi Jain2 months ago

Agar Ai use karte h aap log to customer ki help bhi karni chahiye na

ashis moharana2 months ago

@Meesho_Official Order ID: 309824054880306176 is indicated as "Delivered" but has not been received. Prior interactions with customer service have not resolved the issue. I request either a refund, the return of the product, or a new replacement for my order.

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57,500 views • 28 days ago

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X Freeze

591,858 views • 6 months ago

EVERYONE HAS BEEN TALKING ABOUT AI AGENTS. BUT THE REAL UNLOCK ISN'T BETTER PROMPTS: IT'S BETTER LOOPS. Everyone is chasing the perfect prompt. The top AI engineers are chasing something else: loops. Instead of chatting back and forth with AI, they give it one goal and let it continuously improve its own work until the objective is met. Here's the framework: • Define the goal once • AI discovers the work required • Specialized agents tackle tasks in parallel • A verification agent checks the results • If the goal isn't met: loop again. If it is: deliver. The key ingredient is memory. Each cycle remembers what was completed, what failed, and what still needs work, so every iteration gets smarter instead of starting from scratch. There are two approaches: • Open loops: Maximum autonomy, but higher token costs as AI explores freely. • Closed loops: Clear boundaries, defined success metrics, lower costs, and far more practical for everyday workflows. Don't ask AI to complete a task. Build a system where AI can plan, execute, verify, remember, and repeat until the task is actually finished. Prompt engineering got us here. Loop engineering is what takes AI from assistant to autonomous teammate.

EVERYONE HAS BEEN TALKING ABOUT AI AGENTS. BUT THE REAL UNLOCK ISN'T BETTER PROMPTS: IT'S BETTER LOOPS. Everyone is chasing the perfect prompt. The top AI engineers are chasing something else: loops. Instead of chatting back and forth with AI, they give it one goal and let it continuously improve its own work until the objective is met. Here's the framework: • Define the goal once • AI discovers the work required • Specialized agents tackle tasks in parallel • A verification agent checks the results • If the goal isn't met: loop again. If it is: deliver. The key ingredient is memory. Each cycle remembers what was completed, what failed, and what still needs work, so every iteration gets smarter instead of starting from scratch. There are two approaches: • Open loops: Maximum autonomy, but higher token costs as AI explores freely. • Closed loops: Clear boundaries, defined success metrics, lower costs, and far more practical for everyday workflows. Don't ask AI to complete a task. Build a system where AI can plan, execute, verify, remember, and repeat until the task is actually finished. Prompt engineering got us here. Loop engineering is what takes AI from assistant to autonomous teammate.

Dami-Defi

13,363 views • 3 months ago

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

28,580 views • 4 months ago

The rules of professional product development are being rewritten in real time. - PMs and designers can ship software as easily as engineers. - Software is no longer just built for humans—it’s also built for agents as first-class citizens. To better understand how we build products in this world, I invited Mike Krieger (Mike Krieger) on Every 📧’s AI & I podcast. Mike cofounded Instagram and is now a member of the technical staff at Anthropic, co-leading Anthropic Labs, their internal incubator for experimental products. He's been at the frontier of two transformative technology waves: mobile/social and now agent-native software. We discussed: - How to build a truly agent-native product. The best products today, like Claude Code, allow users to do things that their creators never intended. But that requires hard trade-offs between freedom and safety/reliability for frontier products, an issue that Mike's team is learning how to solve. - What's different about building now versus building Instagram. At Instagram, it took months to hit dead ends and learn what to cut. Now, that cycle runs in hours. - The trap of building too much, too fast with agents. You can go from idea to a nearly-shipped product in a day, but that process doesn’t give you the incremental feedback that used to tell you what not to build. The models are great at adding features, but can create a product that lacks coherence. - How Anthropic Labs structures product teams. New product experiments are led by only two people, usually a product manager or designer paired with an engineer. Mike says bigger teams tend to be too slow because of coordination costs. - Why you need to throw out your product and start over every three to six months. AI progress means most of your harness will be outdated quickly—the best teams build this into their product strategy. And much more! You should watch this one. Timestamps Introduction: What's gotten easier—and what hasn't—about building products in the age of AI: Why vibe coding creates "indoor trees": How rewrites have become a normal part of the development process: What "agent native" product design means: How Mike's labs team is structured and the cofounder model: The best signal for a product bet is someone with "break through walls" conviction: Navigating enterprise customers while keeping pace with rapid AI change: OpenClaw, personal agents, and the product question defining 2026:

The rules of professional product development are being rewritten in real time. - PMs and designers can ship software as easily as engineers. - Software is no longer just built for humans—it’s also built for agents as first-class citizens. To better understand how we build products in this world, I invited Mike Krieger (Mike Krieger) on Every 📧’s AI & I podcast. Mike cofounded Instagram and is now a member of the technical staff at Anthropic, co-leading Anthropic Labs, their internal incubator for experimental products. He's been at the frontier of two transformative technology waves: mobile/social and now agent-native software. We discussed: - How to build a truly agent-native product. The best products today, like Claude Code, allow users to do things that their creators never intended. But that requires hard trade-offs between freedom and safety/reliability for frontier products, an issue that Mike's team is learning how to solve. - What's different about building now versus building Instagram. At Instagram, it took months to hit dead ends and learn what to cut. Now, that cycle runs in hours. - The trap of building too much, too fast with agents. You can go from idea to a nearly-shipped product in a day, but that process doesn’t give you the incremental feedback that used to tell you what not to build. The models are great at adding features, but can create a product that lacks coherence. - How Anthropic Labs structures product teams. New product experiments are led by only two people, usually a product manager or designer paired with an engineer. Mike says bigger teams tend to be too slow because of coordination costs. - Why you need to throw out your product and start over every three to six months. AI progress means most of your harness will be outdated quickly—the best teams build this into their product strategy. And much more! You should watch this one. Timestamps Introduction: What's gotten easier—and what hasn't—about building products in the age of AI: Why vibe coding creates "indoor trees": How rewrites have become a normal part of the development process: What "agent native" product design means: How Mike's labs team is structured and the cofounder model: The best signal for a product bet is someone with "break through walls" conviction: Navigating enterprise customers while keeping pace with rapid AI change: OpenClaw, personal agents, and the product question defining 2026:

Dan Shipper

59,271 views • 6 months ago

SaaS isn’t dead, it just needs to become agent-native. Linear (Linear) is a great example of how: They pivoted the product to be used by both humans and agents, and that has made them one of the premier software tools in the agent-native era. I had Linear’s cofounder and CEO Karri Saarinen on Every 📧's AI & I to talk about how a product management tool for human software developers became an agent-native tool—and how Linear’s trajectory reveals a bright future for SaaS businesses: - Speed means decisions matter more, not less. AI makes it easy to have an idea and build it without considering whether its existence is justified. When ChatGPT was released, SaaS companies were launching their own chatbots left, right, and center. Instead of jumping on the bandwagon, Linear stopped to consider whether the application was useful. (It wasn’t.) - Just because the technology has changed doesn’t mean your mission should. Karri attributes Linear’s success to never losing sight of what matters: helping teams develop great software. Instead of chasing trends, Linear focused on understanding how AI was impacting its customers’ workflows—and updating its product accordingly. - Agents are now first-class users. Linear never tried to change what it was or did well; it just expanded the user base. Companies can now kick off agents inside Linear, manage them, and track what they're working on alongside the humans on the team, which explains why Codex, Coinbase, and Brex all run their agents on Linear. This is a must watch for anyone interested in how an agent-native SaaS company operates. Watch below! Timestamps: Introduction and how Every first discovered Linear: 00:00:39 Why Linear waited to ship AI features instead of rushing to chatbots: 00:02:00 Linear's agent platform and becoming the system that guides AI agents: 00:05:06 Why "SaaS is dead" is a simplistic narrative: 00:07:42 How Linear adopted AI coding tools internally: 00:12:18 AI's impact on product building workflows—speed versus thoughtfulness: 00:17:45 The value of conceptual work and thinking before shipping: 00:22:18 How AI is reshaping Linear's product strategy: 00:29:30 Demo: Linear's agent skills, shared context, and code review workflow: 00:37:18 The future of product development and the enduring role of human judgment: 00:47:48

SaaS isn’t dead, it just needs to become agent-native. Linear (Linear) is a great example of how: They pivoted the product to be used by both humans and agents, and that has made them one of the premier software tools in the agent-native era. I had Linear’s cofounder and CEO Karri Saarinen on Every 📧's AI & I to talk about how a product management tool for human software developers became an agent-native tool—and how Linear’s trajectory reveals a bright future for SaaS businesses: - Speed means decisions matter more, not less. AI makes it easy to have an idea and build it without considering whether its existence is justified. When ChatGPT was released, SaaS companies were launching their own chatbots left, right, and center. Instead of jumping on the bandwagon, Linear stopped to consider whether the application was useful. (It wasn’t.) - Just because the technology has changed doesn’t mean your mission should. Karri attributes Linear’s success to never losing sight of what matters: helping teams develop great software. Instead of chasing trends, Linear focused on understanding how AI was impacting its customers’ workflows—and updating its product accordingly. - Agents are now first-class users. Linear never tried to change what it was or did well; it just expanded the user base. Companies can now kick off agents inside Linear, manage them, and track what they're working on alongside the humans on the team, which explains why Codex, Coinbase, and Brex all run their agents on Linear. This is a must watch for anyone interested in how an agent-native SaaS company operates. Watch below! Timestamps: Introduction and how Every first discovered Linear: 00:00:39 Why Linear waited to ship AI features instead of rushing to chatbots: 00:02:00 Linear's agent platform and becoming the system that guides AI agents: 00:05:06 Why "SaaS is dead" is a simplistic narrative: 00:07:42 How Linear adopted AI coding tools internally: 00:12:18 AI's impact on product building workflows—speed versus thoughtfulness: 00:17:45 The value of conceptual work and thinking before shipping: 00:22:18 How AI is reshaping Linear's product strategy: 00:29:30 Demo: Linear's agent skills, shared context, and code review workflow: 00:37:18 The future of product development and the enduring role of human judgment: 00:47:48

Dan Shipper 📧

36,359 views • 6 months ago

Databricks CEO Ali Ghodsi warns the "SaaS Apocalypse" is real: Barriers to entry have collapsed, and AI agents have already replaced humans as the primary users With a fresh $5 Billion in capital and a $134 Billion valuation, Ghodsi reveals why the traditional software business model is under siege Here is how the AI era is rewriting the rules of the $600B software market: > 81% Agent-Driven: - For the first time, over 81% of databases launched on Databricks are created by autonomous agents, not humans > 10x Dev Speed: - AI has made writing software ten times faster, allowing new competitors to disrupt markets that were once protected by massive moats > The "Advisor" Strategy: - To survive skyrocketing AI costs, companies are switching to "advisor" architectures - using cheap open-source models for basic tasks and only calling "big brains" like GPT-5 when they get stuck > Small Teams Win: - Ghodsi insists that big teams are now a liability; in the AI era, coordination is the biggest bottleneck, and small, nimble squads are the only ones who can move fast enough "Writing software is ten times faster now... your margin is my opportunity" Bookmark & Watch Right now

Databricks CEO Ali Ghodsi warns the "SaaS Apocalypse" is real: Barriers to entry have collapsed, and AI agents have already replaced humans as the primary users With a fresh $5 Billion in capital and a $134 Billion valuation, Ghodsi reveals why the traditional software business model is under siege Here is how the AI era is rewriting the rules of the $600B software market: > 81% Agent-Driven: - For the first time, over 81% of databases launched on Databricks are created by autonomous agents, not humans > 10x Dev Speed: - AI has made writing software ten times faster, allowing new competitors to disrupt markets that were once protected by massive moats > The "Advisor" Strategy: - To survive skyrocketing AI costs, companies are switching to "advisor" architectures - using cheap open-source models for basic tasks and only calling "big brains" like GPT-5 when they get stuck > Small Teams Win: - Ghodsi insists that big teams are now a liability; in the AI era, coordination is the biggest bottleneck, and small, nimble squads are the only ones who can move fast enough "Writing software is ten times faster now... your margin is my opportunity" Bookmark & Watch Right now

Henrikh

211,107 views • 3 months ago