Shreyans Bhansali

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Shreyans Bhansali

Shreyans Bhansali

@askcodi

Building AI stuff I find cool | Makersfuel x AskCodi

Katılım Ekim 2021
868 Takip Edilen698 Takipçiler
Bindu Reddy
Bindu Reddy@bindureddy·
Google Gemini's AI Problem - Flash is a good chat model but is WORSE than Grok on agentic loops. Even simple ones - Pro is a legacy model - Veo is too expensive. SeeDance is better - Nanobanan Pro is old. GPT image is better - Flash Lite has high latency, so it's not really very usable TLDR; Google has no leading AI model for core workloads. Chat is fine but Grok and Luna are pretty good as well and follow instructions better
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Ruben
Ruben@rdominguezibar·
OpenAI's chairman just responded to the Kimi K3 panic 👇 The timeline calls K3 a frontier killer. Bret Taylor's answer on CNBC: every CEO he talks to asks the same 2 questions. 1️⃣ Am I getting value from my token spend? 2️⃣ What's my moat in the age of AI? His counter on cheap Chinese models: frontier models finish the same task in far fewer tokens. Cheap per token means little if you burn 5x more of them The new battleground is tokens per task, and the price war just forced OpenAI to argue efficiency instead of capability
Ruben@rdominguezibar

Kimi's CEO🇨🇳 says every AI lab has the wrong obsession Zhilin Yang, on why K3 beat the frontier: "Every lab, like Claude, thinks the model matters most. That's wrong. It's how you organize the people building it that wins." Then Moonshot proved the philosophy. K3 sold out every plan on purpose, cutting their own revenue instead of throttling existing users. When Anthropic hit that wall in April, they cut users' usage 50% at peak. His one big idea: long context is the AI era's RAM. The 128K-to-gigabytes jump, compressed into 2 years instead of 40. The real moat: "your biggest advantage, perhaps your only advantage, is your organization." Model, or the team behind it: which actually wins?

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jia
jia@jia_seed·
i now find myself surprised when actual quality talent comes along, in as wait you actually want to join.. us? i think building momentum is the best convincing factor for great talent. this person has high agency, i’ve seen their work on the internet and honestly honored they’d want to join
jia tweet media
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Rohan Paul
Rohan Paul@rohanpaul_ai·
Aravind Srinivas on why China’s open-source AI may become more powerful than ever. And why Anthropic has lobbied very hard for export control. "the only reason why there is even a 12-month gap between open source and frontier models is export controls. But there is a chance that, because of that, they now get really good at the physical layer. One advantage they (China) have is that they can actually build data centers a lot faster. Power is not a problem. Permits are not a problem. People are not a problem. Labor is not a problem. Expertise is not a problem. And so, by forcing them to go out there and build all this, you are converting them into a far more potent competitor." --- From "20VC with Harry Stebbings" YouTube channel ( @HarryStebbings ), link in comment
Rohan Paul@rohanpaul_ai

Jensen Huang explained how blocking China from Nvidia does not anymore means blocking China from AI. If you don’t give your competitor the best chips, they will lag behind. This is how export control stories go. China is not waiting at the door of American computer systems anymore. Huawei’s rise is an example of a ban becoming an economic boost. It creates a market and teaches domestic suppliers how to get stronger, grow and export. Now the real battle is over who controls the chips, the talent, the energy, the infrastructure, the models, the apps and the whole intelligence stack. Thinking of chip policy as a valve that can be opened and closed is wrong. Each barrier slows down one flow while speeding up another. In the long run, the threat may be to a world in which American technology is absent from the systems that the US wishes to change. --- From "Fox Business" YouTube channel, (full video link in comment)

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Rohan Paul
Rohan Paul@rohanpaul_ai·
Larry Ellison on the AI moat, makes more sense now. AI is commoditizing because models use the same public internet data. True competitive edge isn't the model itself anymore, but access to exclusive, proprietary datasets. That is the only moat left.
Rohan Paul@rohanpaul_ai

Great explanation by Emad Mostaque, co-founder of Stability AI. "We’ll see the cost of Kimi K3 drop by 10 to 50 times, I think, over the next few months as it gets optimized. " Basically Kimi K3’s current inference cost is quite high, but that price reflects immature infrastructure, not a permanent technical limit. And that gap will not last long. US-based specialized infrastructure companies will optimize kernels, routing, quantization, batching, memory use, and serving systems around those models once the Kimi K3 weights are available. --- "Right now, it uses twice the number of tokens for the same task compared with GPT-5.6. Again, we’re going to see that cost drop because everyone and their dog is going to optimize the crap out of this. Fireworks has just raised funding at a $17 billion valuation, while others, such as Modal and Baseten, are valued at $10 billion. These are inference providers for open-source models. They’ve all raised around a billion dollars, which they’re now going to spend on optimizing the Chinese model, making it more efficient, and running it. American labs that handle the inference side of things are going to optimize the crap out of this. Therefore, we will see it catch up." ---- From "Peter H. Diamandis" YouTube channel, (full video link in comment)

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GREG ISENBERG
GREG ISENBERG@gregisenberg·
How to become a $1M forward-deployed engineer (FDE) in 30 days (and what FDE clearly means): - What an FDE is and why the role EXPLODED - The 3 stages of the job (business reality, judgment, building) - The exact 30 day roadmap to become one - What the top roles actually pay
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Lenny Rachitsky
Lenny Rachitsky@lennysan·
I asked @Netflix's CPTO Elizabeth Stone what skills are trending up with the rise of AI. Her answer: systems thinking.
Lenny Rachitsky@lennysan

Elizabeth Stone's first visit to the podcast was my second most popular episode of all time (right behind @bchesky). Her second visit is already trending far beyond that. A lot has changed since I first spoke with Elizabeth 2.5 years ago. She was promoted to CPTO at @Netflix, taking on product and design orgs, on top of the engineering and data orgs she already led. Also, AI. In our in-depth conversation, we discuss: 🔸 Why all the top AI labs converged on Netflix’s culture 🔸 Why Netflix is hiring more "systems thinkers" — and fewer narrow specialists 🔸 How she protects quality when AI makes output nearly infinite ("If I had more time, I would've written a shorter letter") 🔸 The most impactful AI use cases internally 🔸 How Elizabeth builds "excellence as an operating system" Listen now 👇 youtu.be/t0GiTyz4syY

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Movez
Movez@0xMovez·
Andrew Ng: "100% of my tasks are now done by AI agents - hype has exceeded my expectations. Loops is next step. in 3-6 months, everyone will be using self-improving loops. No more prompting." In a 30-minute talk, Andrew Ng explains how to build self-improving agentic systems from scratch. Worth more than a $500 agentic course.
Movez@0xMovez

x.com/i/article/2067…

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Ricardo
Ricardo@Ric_RTP·
Chinese AI models are wiping billions off Big Tech right now. Google just lost $200 billion in a single day, and the model it needed to fight back still isn't ready. Gemini 3.5 Pro, Google's most powerful model, is months behind schedule. Alphabet stock dropped 4.4% that same day. The Deepseek moment is happening again, and the new model is FAR bigger. On the same day Google's delay leaked, a Beijing lab called Moonshot released Kimi K3. It is the largest open model ever built, with 2.8 trillion parameters. It took the number one spot on the Frontend Code Arena, a live coding leaderboard, passing Anthropic's best model. And Moonshot is giving it away for free on July 27. The genius part: Anyone with enough computers can download it and run a frontier level AI without paying a cent to a US company. A single task on Kimi K3 costs about 94 cents. The same work on some American models costs nearly double. So why would a company keep paying premium prices for a model it can now get for free? The entire US AI business is built on selling access to models that cost billions to train. If a free Chinese version does most of the same work, that pricing power starts to crack. And Kimi is close to the best. On one closely watched intelligence ranking it scored 57, just behind the top American models GPT-5.6 Sol and Fable 5, and ahead of Claude Opus 4.8. Bank of America told clients that Kimi proves Chinese labs can keep making big leaps even with limited chips. And the founder of Moonshot, Yang Zhilin, learned to build AI as a researcher INSIDE Google. Google literally wrote the 2017 paper that made all of these models possible. Now the people who studied its work are using it to destroy Google, and handing it out for free. What happens next: Kimi K3's weights go public on July 27. Google reports earnings on July 22, and everyone will be asking the same question about Gemini. If free models keep topping the charts, every valuation built on paid AI access has to be rewritten. What do you think?
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Damian Player
Damian Player@damianplayer·
OpenAI's chairman Brett Taylor on the new Chinese model Kimi K3. the timeline thinks it's a frontier killer. he says every CEO he talks to asks the same two things: am I getting value from my token spend, AND what's my moat in the age of AI. on token efficiency, he says the frontier models still finish the same task in far fewer tokens. worth a listen.
MTS@MTSlive

SITUATION BREWING: The Trump administration is considering restricting cutting-edge Chinese AI models, with momentum reviving after the launch of Kimi K3, per Axios.

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darkzodchi
darkzodchi@zodchiii·
The creator of Claude Code, Boris Cherny: "Every night I have hundreds, sometimes thousands of agents running 5, 10, 20 hours. That's just how engineering is done now." Bloomberg just gave him an hour to unpack it: not better prompts, but loops. The same pattern runs on both Claude Code and Kimi K3. Watch the talk, and save the full setup below 👇
darkzodchi@zodchiii

x.com/i/article/2079…

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Lenny Rachitsky
Lenny Rachitsky@lennysan·
My biggest takeaways from @Netflix's Chief Product and Technology Officer Elizabeth Stone: 1. Elizabeth believes that “systems thinking” is becoming the most important skill in the AI era. In engineering and product, this means people who can see across business domains and build the common capabilities that let many teams move quickly. In design, it means experience designers who create templates and design systems so that non-designers can ship work that stays coherent and on-brand. The underlying driver is velocity: when more people are doing more types of work at higher speed, you need to be good at building common scaffolding. 2. Systems thinking is learnable: zoom out one level from your specific problem. Given a task, step back one click—what bigger problem does this serve the business, will it scale across the product surface areas, should it become a platform capability? The companion habit: do your job in a way that helps your manager do theirs. This will force you to think about how all the pieces fit together. 3. Expect a storming phase before a forming phase. The role confusion people feel right now (“What is my job anymore?”) is the predictable middle of any transformative technology. Elizabeth’s advice: focus on high-quality source-of-truth data, guardrails on what ships, and constant internal reinforcement that humans own what they create. 4. The top AI labs converged on Netflix’s culture. High agency, high talent density, top-of-market pay, bottom-up thinking, fast experiments—the traits Lenny hears constantly from AI labs were in Netflix’s early culture deck. Elizabeth’s explanation: excellence comes from hiring exceptional people, trusting them to do great work, and holding them accountable. 5. Netflix’s culture is centered around building “excellence as an operating system.” High talent density, radical transparency, context not control, and the keeper’s test. These work together to create an environment of trust and accountability, without bureaucracy. But it’s also uncomfortable. It requires tolerating people making decisions you’d make differently, resisting the reflex to add process when things go wrong, and letting people carry the weight of their own choices. Elizabeth describes the hardest part as “being comfortable in that discomfort.” 6. The keeper’s test is as much about recognizing great people as it is about removing the wrong ones. The test—“If this person told me they were leaving, would I fight to keep them?”—is often cited in its difficult form: the moment you realize someone isn’t the right fit. But Elizabeth uses it predominantly as an entry point for honest performance conversations that are deeply positive. Most of the time the answer is “I would fight so hard to keep you,” which creates the opening to articulate strengths, discuss impact, and name what’s working. Good feedback hygiene needs a forcing function; the keeper’s test provides one. 7. Specialization is trending down—adaptable generalists are trending up. We’re shifting away from narrow stack-layer specialists (pure frontend, pure backend) toward people who can navigate fluidly across layers. The same logic applies to business domain knowledge: the mindset of “I’m a payments expert, full stop” is less valuable than “I know payments well enough and I’m willing to imagine what the future version of this looks like.” The meta-skill is learning to learn, not locking into a single lane. 8. Netflix’s approach to AI fluency is a universal principle, not a level-specific expectation. Rather than rewriting career ladders to specify what AI competence looks like at each level, Netflix added a single aspiration across all roles and levels: AI fluency. What fluency means varies by function and seniority, but the non-negotiable minimum is the same everywhere—an open-minded, experimental mindset, genuine curiosity, and comfort with ambiguity.
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jia
jia@jia_seed·
bruh there are 4 major companies that have hired the majority of the tech timeline i see all the new hires with their newly minted logos and it feels like a harry potter houses thing
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Brian Halligan
Brian Halligan@bhalligan·
“We disagree a lot. Like, all the time.” When you're building a company, you don't need constant harmony. You need a mirror. Someone to expose your blind spots, challenge your logic, and stand up to you. And vice versa. @kalshi CEO @mansourtarek_ on his dynamic with his co-founder @luanalopeslara: “We disagree by design." The best founding teams aren't perfectly aligned. They're productively oppositional. Tag your mirror below 👇 (Full episode in the comments)
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Harry Stebbings
Harry Stebbings@HarryStebbings·
Biggest lesson from working with Jensen Huang on leadership “Leadership is just judgment. It's not privilege, it's judgment. You need the right context to make the right judgment for the team. In a high-velocity space, if you don't know what works, what doesn't, what the gaps are, you make the wrong call. You cannot wait, because there is always information loss in transition. I need to know what's happening on the ground to make the judgment for the company.” @lqiao
Harry Stebbings@HarryStebbings

Everyone gets angry with me for saying triple, triple, double, double is dead. Fine, I do not really care. Venture is about investing in unbelievable outliers. Anomalies that own markets with generational founders. @FireworksAI_HQ is an example of this. They scaled to $1BN in ARR in just 3.5 years. They also have just 200 employees making it an insane $5M revenue per head. @lqiao just raised a whopping $1.5BN at a $17BN valuation and I sat down with her. Added my notes and the episode below: 1. The Challenges That Come From Such a Fast Development Cycle for Chips Hardware innovation is moving so quickly that rapid SKU cycles now outpace traditional depreciation timelines, changing the financial calculus of building versus renting infrastructure. Founders should prioritize growth and market agility over immediate gross margins, avoiding premature optimization until customer workloads stabilize. 2. Why National Sovereignty Is Real in AI and Every Company Should Have Its Own Model Frontier models function like a society’s core electricity grid. Relying entirely on a third-party API creates the existential risk of sudden disconnection, making model ownership and infrastructure independence critical for both sovereign nations and enterprise businesses. 3. Why the Future Is Millions of Specialized Models Frontier providers bake their own design tastes and values into models, which inevitably misaligns with enterprise business logic. The future belongs to millions of specialized, “one-size-fits-one” models tailored to proprietary data, consistently outperforming generalized AGI on accuracy, speed, and unit economics. 4. The Transition From the Year of Coding to the Year of Co-Work AI adoption has rapidly evolved from engineering-centric coding tools to a diversified ecosystem of B2B co-work agents. Founders and VCs must look past crowded developer environments to capture massive value in specialized workflow automation across legal, finance, healthcare, and other enterprise functions. 5. How a 10x Cost Reduction Will Drive a 100x Explosion in Usage Temporary supply chain backlogs will eventually ease, compressing infrastructure and model-tuning costs by 10x over the next three years. This deflation in token unit economics will turn intelligence into a near-frictionless commodity, driving a massive surge in enterprise production usage. 6. Why Avoiding the Application Layer Is Essential for Platform Focus Platform defensibility requires strict focus on multi-chip agility without creating vertical hardware or software dependencies. By refusing to move up into the application layer, infrastructure platforms avoid competing with their own ecosystem and maximize their value in specialized model orchestration. 7. Biggest Lesson From Working With Jensen Huang on Leadership Leadership in hyper-velocity markets is defined by rapid judgment, not executive privilege. Because critical information degrades as it moves through layers of corporate hierarchy, leaders must stay close to ground-level technical details to maintain execution speed and avoid flawed strategic calls.

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Gergely Orosz
Gergely Orosz@GergelyOrosz·
"This was a massive undertaking that took well over a year." Native app development was hard, is hard, and will probably remain hard for complex apps, for good reason. AI helps with a lot of stuff, but it won't magically solve hard engineering problems, exhibit #1
Nikita Bier@nikitabier

Today we're announcing the completion of one of the largest engineering projects in the company's history: We rebuilt the X Android app from scratch It's faster, smoother and more reliable. But most of all: it will enable us to build new features at lightning speed.

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Vijay Iyengar
Vijay Iyengar@vijayiyengar·
At what point will the labs acquire mid-size, dying SaaS companies just to use their Slack as an RL gym? Rationale: - Knowledge work is the most economically valuable market for AI at the moment - Data is a big gap, primarily because knowledge work is done inside of companies, not publicly - Pre-AI SaaS companies are trading at 2-3x revenue, many at ~$1-2B valuations - They have 10-15y of internal data sitting in their Slack, Github, Google Docs, etc of how teams collaborate to build and sell products. This is worth nothing to most people, but a lot to the labs, since it's data they've never seen before. The problem might be that these companies are still too expensive and that the data is not precisely tuned to what the models need. But it's an interesting, if dystopian, thought exercise.
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