Knowledge only counts when it moves you to act.
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Starting this blog to share quick thoughts and ideas. Stay tuned!
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Balancing curiosity and discipline is tougher than it looks. Lean too much into curiosity and you lose focus, but lean too much into discipline and you lose the spark of discovery.
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“The impediment to action advances action. What stands in the way becomes the way.” — Marcus Aurelius
True freedom is not in controlling events, but in controlling our response to them.
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The education ecosystem should leverage software tools to enable a more mindful and well-structured learning experience.
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Effective teaching is not merely the delivery of content; it is the design of experiences that place students at the center of their own learning.
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“The literal meaning of life is whatever you’re doing that prevents you from killing yourself.” — Albert Camus
The philosophy of Absurdism is quite interesting. It offers a balanced way to see life, avoiding both nihilism and blind belief. As explored by Albert Camus, meaning isn’t given to us but created through our actions and persistence. It encourages us to keep questioning, keep searching, and consciously choose how we live.
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Teaching people to seek truth, think critically, and practice empathy helps build a thoughtful, resilient, and compassionate society.
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Blending auditory and visual inputs, like listening to text while reading, can be a real game-changer for readers with ADHD, and my go-to tool for this is ElevenReader.
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Externalizing memory helps reduce mental load, keeps your ideas organized, and makes it easier to revisit and connect insights. It’s something everyone could benefit from.
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I really dislike how Meta designs its platforms. The whole architecture feels unfriendly to privacy, as if the idea of personal boundaries online barely exists. Everything seems built to keep users exposed rather than protected.
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Software should empower users, letting them customize, extend, and own their experience.
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Shutting down Windows Phone was definitely a major misstep for Microsoft.
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Netlify’s new credit-based pricing does not make much sense to me, especially as a hobbyist. Charging 15 credits per production deploy feels unnecessarily restrictive.
I’ve decided to switch to Cloudflare Pages.
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Software should be reimagined with AI agents as true first-class citizens. Instead of treating them as add-ons, we need to design systems where they have their own identities, memory, goals, and the ability to collaborate naturally with humans.
The AI age needs software built for intelligence from the start, not awkwardly retrofitted later.
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I understand why GitHub is shifting toward token-based billing. The per-request setup just did not hold up anymore. It treated a quick “explain this function” the same as something massive like “analyze and refactor my entire codebase,” even though those tasks require completely different levels of compute.
GitHub did have a multiplier system where each request was weighted based on the model used, so using something like Claude Opus might cost 3x or 4x a single request compared to a cheaper model. But the multiplier only accounted for model choice, not the actual size of the work. So if you asked Opus a quick one-line question, you still burned the full 3x or 4x multiplied request—the same as someone running a massive multi-file refactor on the same model. You were essentially paying a premium request cost for a few tokens of actual work.
Token-based billing fixes this directly, since a small interaction barely touches your balance regardless of which model you use, and larger tasks scale naturally with their actual compute footprint.
Another issue was that, in the request-based system, there was obviously no concept of input tokens, output tokens, and cache usage. All of it was effectively bundled into the same “request” abstraction, which blurred the real cost of what was happening under the hood. That was not particularly fair for GitHub, which had to absorb unpredictable compute loads.
Token-based billing makes things much fairer for both users and GitHub.
That said, GitHub still needs to make these plans worth it.
In the long run, Microsoft likely needs to develop its own AI models rather than relying entirely on third-party providers. A practical starting point would be building on top of open-source models and improving them through reinforcement learning and product-specific training, similar to how some AI coding tools have customized open-weight models for coding workflows. This would allow Microsoft to gain experience operating and refining models while reducing costs and dependence on external vendors.
Over time, those efforts could evolve into fully in-house frontier models that power GitHub Copilot directly. If Microsoft can build models that are competitive with the leading providers, it would gain greater control over pricing, margins, product direction, and the overall developer experience.
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Top-tier AI consumes too much compute to fit sustainably into flat-rate subscriptions. Bundling these models forces a difficult tradeoff: providers must either hike the monthly price or drastically reduce the allocated usage credits to maintain their margins. Consequently, lighter models will remain in affordable subscription tiers, while advanced models transition strictly to metered billing.
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Google is fundamentally a product company, which explains its strategy of launching specialized AI applications, like Gemini, Antigravity, and Flow, rather than pursuing the “super app” model favored by OpenAI or Anthropic. While this decentralized approach aligns with Google’s DNA, they must be careful to avoid over-fragmenting the ecosystem. To prevent user confusion and value dilution, Google should keep its standalone apps to a minimum, ensuring each serves a distinct, highly focused niche.
Crucially, Google should implement a unified AI quota system tied to a Google One AI subscription. Pooling usage limits across the entire ecosystem would be a win-win: users get the flexibility to spend their credits on their most-used apps without feeling restricted, while Google mitigates the massive compute costs of users maxing out separate quotas across a dozen different tools.
To further drive adoption and retention, Google needs to offer more generous credit allowances in their paid tiers while doubling down on UI/UX, performance, and overall polish. Elevating the design and functionality of these specialized apps is what will transform them from a scattered toolkit into a cohesive, premium ecosystem.
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The sudden restriction on Fable will serve as a major wake-up call for the industry. In response, demand for open-weight alternatives will surge. Fearing sudden lockouts from proprietary models, organizations will aggressively shift to open-weight AI to guarantee uninterrupted access for their critical workflows.
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In 2026, the tech industry is expected to experience a sharp divergence in employment trends: product companies will likely continue workforce reductions, while IT services firms ramp up hiring amidst significant internal restructuring. This shift is fundamentally driven by AI. For product companies with fixed portfolios, AI-driven efficiencies mean fewer developers are required to build and maintain existing software. Conversely, while AI brings similar efficiencies to IT services, it also drastically lowers the overall cost of development. This price drop makes digital transformation accessible to a much broader range of businesses, creating a surge in project volume that will drive IT services firms to expand and adapt their workforce.
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The Future of Machine Learning
Today’s AI models are minds without bodies. They speak, reason, see, and picture things internally, a set of faculties roughly comparable to a child’s. What they lack is everything a child acquires by acting in the world.
The next step is to give that child more skills.
Watching is where it starts
Egocentric learning uses a first-person, eye-level perspective, typically from a wearable camera, so the model learns from the same viewpoint it will eventually have to act from. Seeing a task performed and seeing it from the doer’s position are different lessons.
Imitation learning is the level above this. Egocentric data supplies the right view; imitation turns that view into behaviour. Watching becomes doing.
This is also where the world model kicks in: an internal, predictive account of how the environment behaves. Copying a motion is shallow. Predicting what that motion will cause is what makes the skill transferable.
But imitation has a ceiling. A learner who only copies can never exceed the demonstration. Real skill comes from constructivist learning, building understanding through experience, and its practical extension, constructionism, building understanding by making things. Facts can be transmitted. Skills have to be constructed.
The material for this already exists. People use these models constantly for mental work: code generation, chat, image generation. Here the environment isn’t matter, it’s a compiler, a test suite, a conversation, anything that answers back. Robots will generate the same volume for physical work through embodied learning, building knowledge by physical interaction via sensorimotor systems, rehearsed in simulation and then tested against real sensors and actuators, where prediction meets friction, weight, and failure. Simulation teaches the shape of a task. Only the real world teaches its cost. Every one of those interactions, mental or physical, is a test case with a real outcome attached. Turning that stream into skill requires two things: constructivist learning to produce the experience, and continual learning to update the weights from it.
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