We've traditionally thought of programming as carefully translating our ideas line-by-line into code. But what if writing explicit code isn't the endgame, but just an intermediate step?
With the recent breakthroughs in LLMs, we're entering a new paradigm — one where we specify intent clearly, contextually, and visually, allowing an LLM to compile that intent directly into executable code.
Here are the guiding principles I've found essential for this new way of building:
Every interaction with an LLM must preserve context — intent, previous conversations, or metadata. Much like traditional compilers rely on context (types, scopes), LLM-compilers thrive when context is explicit and interlinked, ensuring accurate, robust outputs.
Tools must facilitate frictionless collaboration. Rather than isolated coding sessions, codebases become shared conversations. Think real-time editing, visual brainstorming, and seamless versioning — allowing teams to evolve ideas effortlessly.
Complexity should always be optional. Interfaces should be minimal, intuitive, and delightful, instantly showing value. Advanced depth, like debugging visual logic flows, remains accessible but unobtrusive.
Provide clear entry points for novices and deep layers for advanced users. Shield beginners from unnecessary complexity, but enable experts to dive deep into underlying logic, debugging, and refinement.
Purely textual input isn't enough. Richer, visual forms of specification — like visual grammars inspired by Pāṇini’s linguistic models — better align with how we naturally think, communicate, and express intent.
The shift here is subtle yet powerful: code becomes an output of clearly expressed intent, not its input. This accelerates iteration, fosters deeper collaboration, and expands the potential of software development itself.
I've started exploring this new programming paradigm through an experiment called Textile.
It's my way of testing how we can practically implement visual, intuitive, and collaborative tools for specifying intent beyond text alone. We're moving toward a future where programming is more human — visual, intuitive, collaborative — and ultimately, far more powerful. I'll share more about textile soon. Stay tuned.
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.
Microsoft recently announced the Majorana-1 quantum processor, hailed as a breakthrough in quantum computing. Media coverage often glosses over the details, so I set out to deepen my understanding by breaking things down step by step:
A laptop or phone processes information using bits (0s and 1s), like on/off switches. Every calculation is a sequence of bit flips. Even supercomputers work this way—just much faster and in parallel.
However, some tasks (like simulating protein molecules, cracking encryption, or solving huge optimization puzzles) require checking every possibility one by one, which can take thousands, millions, or even billions of years. And there’s a limit to how many processors we can build.
Quantum computers use quantum bits (qubits) that, thanks to superposition, can be both 0 and 1 at the same time. They also use entanglement, where changing one qubit instantly affects another, no matter the distance.
These principles allow quantum computers to perform certain computations exponentially faster than classical ones.
Classical Approach:
Quantum Approach (Grover’s Algorithm):
This exponential speedup is what makes quantum computing revolutionary.
Classical bits are robust and can be copied for error correction. In contrast, qubits, which exist in delicate superposition states, can be easily disturbed by heat, vibrations, or electromagnetic noise. The no cloning theorem prevents copying qubits for redundancy, meaning errors are a significant challenge.
Imagine electrons moving in tiny circular loops on a flat 2D material under a strong magnetic field. These loops form organized “lanes” called Landau levels. Think of it like cars on a racetrack:
Translating this to quantum physics:
By braiding quasiparticles—that is, moving them around each other in specific sequences—we encode information into the topological structure itself. This spreads the information out over the system, making it much more resistant to errors and noise.
Even though Grover’s Algorithm dramatically reduces search time, current quantum computers require massive error correction because systems like those from Google and IBM use fragile superconducting qubits. Tiny disturbances force these systems to use hundreds or thousands of physical qubits to form one logical qubit.
Microsoft’s Majorana-1 processor takes a different approach with topological qubits. Based on Majorana zero modes, these qubits encode information in a way that’s intrinsically resistant to errors. By spreading data across a braided network of quasiparticles, they require far fewer qubits to perform the same work.
While supercomputers scale linearly by adding more processors, quantum computers with topological qubits scale exponentially, opening new frontiers in AI, materials science, and cryptography.
Microsoft’s Majorana-1 processor isn’t just a technological novelty—it represents a major leap in our quest for practical quantum computing. By harnessing topological qubits, we move toward a future where fragile quantum states are replaced by robust, error-resistant systems.
This breakthrough could drastically reduce the resources needed for quantum computation and unlock transformative possibilities across industries.
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.
We’ve LLM-powered Microsoft VSCode. Engineers now build at speeds that were once unimaginable—with @cursor_ai and @windsurf_ai leading the charge, development is set to redefine what’s possible.
We’ve also upgraded Figma with LLM capabilities. Tools like Lovable, Replit Agent, and Bolt.new let PMs and designers prototype at lightning speed, while engineers implement those ideas seamlessly. The gap between concept and execution is disappearing.
Meanwhile, giants like @meta and @google are training models on their entire codebases. Expect these tools to be open sourced—just like Llama and @reactjs. When local models on apple MLX or GPUs beat the latest from @openai or @anthropic in speed, cost, and efficiency, proprietary platforms like @cursor_ai will lose their edge.
Our current chat UX is outdated. It’s a clunky back-and-forth that won’t scale for complex or long-running tasks. We need a new framework—one that treats coding tasks like data pipelines, with built-in measurement, monitoring, and auto-recovery. New open protocols, similar to @anthropic’s MCP, will set that standard.
This isn’t just an upgrade—it’s a complete rethinking of software development. The next breakthrough won’t just generate code; it will transform ideas into action, just as C++ did for assembly and Python for C. Open source models are tearing down barriers, fueling a surge of creativity. Engineers will evolve from code writers to orchestrators of AI systems that handle complexity.
At the end of the day, our tools are holding us back. What we need is a new kind of platform where vision goes beyond text, letting anyone articulate their vision, prototype interactively, and iterate in real time.
An IDE for ideas.
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.
AI is rewriting the rules of tech dominance, and Mark Zuckerberg is playing a high-stakes game to keep Meta in the fight. Once an unassailable titan of social media, Meta now faces slowing growth, privacy-driven advertising challenges, and fierce competition from emerging platforms. In response, the company has taken a bold step by open-sourcing its large language model, Llama. Far from a simple technical decision, or a gesture of corporate benevolence, this move reflects a strategic pivot aimed at revitalizing Meta’s platforms, building a robust developer community, and pioneering immersive AI-driven entertainment. The stakes couldn’t be higher: if Meta fails to adapt in the next five years, it risks sliding into irrelevance in an industry defined by breakneck innovation.
For over a decade, Meta’s social media empire was powered by an advertising model that thrived on virtually unbounded user growth. That momentum has now slowed. Daily active user figures for Facebook increased by a mere 3% year-over-year in 2023, while Apple’s privacy policies have undercut advertising effectiveness. Younger demographics flock to TikTok for fresher, more engaging content, and regulatory scrutiny over data practices looms large. These challenges underscore the fragility of a once-unshakable empire and create a pressing need for radical reinvention.
At the same time, generative AI is flooding the internet with highly personalized content at near-zero production cost. While this democratizes creation — allowing anyone with a smartphone to produce tailored videos or articles — it also risks overwhelming users. Anecdotal evidence suggests that passive consumption of repetitive, AI-generated content leads to ennui, and eventually, disengagement. As with the streaming wars, where only a handful of shows achieve mainstream visibility, a glut of AI created content could drive users to seek more authentic, interactive experiences driven by human connection.
Meta recognizes this: the days of infinite scrolling are numbered. Active participation will be the key to sustaining user interest.
Confronted with slowing growth and a looming user-content fatigue cycle, Meta has centered its rejuvenation strategy on Llama, its open-source large language model. This initiative aims to address three critical objectives:
I. Mitigating Regulatory Risks: By making Llama openly available, Meta positions itself as a collaborator rather than an impenetrable gatekeeper. The company’s transparency could soften antitrust scrutiny, much like Google’s open-source Android platform helped counter claims of monopolistic practices. In an era when lawmakers and the public alike question Big Tech’s concentration of power, open-source moves signify a willingness to share technology rather than hoard it.
II. Cultivating a Developer Ecosystem: Open-sourcing Llama invites developers worldwide to innovate on Meta’s platforms. This collaborative model parallels how Linux, once just a kernel, grew into the backbone of modern computing thanks to community contributions. Likewise, frameworks such as Next.js soared in popularity when fully open-sourced, funneling talent and goodwill back into Vercel’s ecosystem. By nurturing a global community of enthusiasts and professionals, Meta can harness a network effect that boosts Llama’s capabilities and fortifies the company’s tech influence.
III. Powering Immersive Experiences: The third pillar is Meta’s bet on AR and VR — elements crucial to its metaverse vision. Having invested more than $36 billion in Reality Labs since 2019, Meta aims to create interactive worlds where AI adapts to user input in real time. By granting developers access to Llama and its eventual multi-modal successors, Meta hopes to spark innovations reminiscent of Microsoft’s Muse, a generative AI graphics engine that can instantly craft interactive game scenarios. Such technology not only engages users but also counters the fatigue of endless passive scrolling, aligning perfectly with Meta’s push toward more immersive, human-centric entertainment.
Meta has a limited timeframe — around five years — to prove that its metaverse vision and open-source AI strategy can gain mainstream traction. Competitors like Apple have their own AR devices, while nimble startups and large companies alike continue to advance AI applications at a rapid pace. If Meta fails to capture a critical mass of users and developers soon, it risks ceding this emerging frontier to rivals.
By open-sourcing Llama, Meta is making a calculated wager on developers, transparency, and interactive AI experiences to redefine its future. Should Meta succeed, it will have transformed itself from a social media empire under siege to a trailblazer in AI-driven entertainment. If it fails, it will stand as a cautionary tale of how even the mightiest tech giant can be left behind in a world that never stops racing forward.
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.
When people think of a country showcasing its technological might, they might recall the early days of nuclear breakthroughs or iconic space missions. Those were the gold standards of another era. AI foundation models — the kind that can consume as much power as an entire city and cost millions to train — are rapidly becoming the new index of global influence. It’s no longer just about coding; nations are building high-performance computing platforms, modernizing their power grids, and revamping manufacturing, all in pursuit of AI leadership for the next century.
Training and serving a large foundational AI model can be as significant a leap as splitting the atom or sending a satellite into orbit — only now we measure progress in exaflops (floating-point operations per second). For instance,
• European Union (EuroHPC): Allocating nearly €14 billion for exascale systems by 2027, signaling a continent-wide determination to achieve HPC sovereignty.
• China: Aiming for ten exascale systems this year, fueled by advanced domestic chip manufacturing — showing how quickly HPC can evolve when strategically prioritized.
• India: Investing $1.2 billion in a “common computing facility” featuring more than 18,000 GPUs. India now seeks to become a “global AI contender.”
• Brazil: Committing $4 billion to its “AI for the Good of All” plan, underlining a broader push for indigenous innovation rather than relying on external solutions.
These moves are the modern equivalent of major nuclear test launches: you simply can’t hide that level of compute power.
Like constructing a nuclear reactor or placing a rocket in orbit, developing an exascale AI model demands a robust ecosystem:
• Semiconductor Manufacturing: Custom chips, advanced cooling systems, and reliable hardware supply chains become indispensable.
• Energy Infrastructure: Power grids must remain stable when thousands of GPUs switch to full throttle.
• Data Centers: Facilities designed for high-performance computing can handle the extreme heat and throughput of cutting-edge workloads.
• Skilled Workforce: Engineers, researchers, and data scientists who know how to leverage HPC capabilities for real-world impact.
Each piece drives a cycle of innovation — similar to the way the Indian Space Research Organization’s satellite program spurred advances in electronics, miniaturization, and a stronger STEM pipeline.
Historically, countries that mastered advanced technologies — whether nuclear energy or space exploration — did more than just build reactors or land on the Moon. They demonstrated formidable industrial capacity and ignited breakthroughs in materials science, computing, and education. AI foundation models now serve that same role. They accelerate progress in sectors from healthcare to climate research, while enhancing a nation’s standing through intangible soft power.
And just as it was risky to lag behind in nuclear or space pursuits, it’s equally dangerous to trail in AI. Perpetual dependence on foreign HPC or rented AI services cedes data autonomy and future strategic advantage. On the flip side, investing in homegrown HPC infrastructure and training AI models domestically redefines an economy, retains valuable expertise, and anchors global influence — mirroring the impact of major technological races in the past.
We once measured a nation’s technical clout by how many rockets it could launch or the scale of its atomic research. Today, the yardstick is exaflops and the sophistication of its AI models. Foundation models aren’t just about building “fancy AI.” They embody national pride, economic transformation, and a decisive claim on the global technology map. Where earlier tests shook remote deserts or soared beyond Earth’s atmosphere, today’s “test sites” are data centers packed with million-dollar GPU racks.
Just as with past landmark races, those who commit now will shape the direction of AI, and set the rules the rest of the world follows.
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.