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Perspectives on AI, product strategy, builder culture, and what it takes to ship what's next.
AI is automating the analyst layer that consulting has run on for fifty years. Here is what the structural response looks like at the firms building it first.
Read more →AI has collapsed the cost of building software, and buyers now expect systems, not slides. Here is why the service firm delivery model is changing.
Read more →Re-identification, jersey OCR, and team affiliation must all be solved from broadcast footage at once. Identity is what separates tracking from understanding.
Read more →Nostalgia engages the brain's memory, reward, and self-reflection networks. That makes it a psychological resource and a design challenge for immersive media.
Read more →Four concrete moves every product team needs to make as AI-native software becomes the default: tool surfaces, evals, retrieval, and mixed human-agent UX.
Read more →Multi-object tracking in football breaks linear motion assumptions. Association quality, not detection, is the real engineering bottleneck.
Read more →AI-native products shift SaaS economics: value moves to workflow completion, costs turn variable and model-driven, quality needs continuous management.
Read more →Reconsolidation means retrieving a memory can change it. For AI systems that reconstruct the past, every replay is a potential rewrite.
Read more →Ball detection is a deceptively hard problem in sports computer vision. Motion blur, occlusion, and pitch line confusion push specialised models far beyond what general-purpose detectors can handle.
Read more →From YOLO to football-specific transformers, player detection is evolving to handle the unique challenges of broadcast football footage.
Read more →We analysed 7,000+ IT staff augmentation companies to map productised services readiness in a $128 billion market. Most providers are indistinguishable. The few that have productised are pulling away.
Read more →When agents call tools, execute code, and act on external data, traditional security guarantees break. Trust engineering is the discipline that fills the gap.
Read more →Football analysts don't need the highest mAP score. They need reliable, fast, and actionable intelligence from the video already in front of them.
Read more →Most software has no API. Computer use agents close that gap by seeing and operating UIs directly, unlocking the long tail of automation that APIs can't reach.
Read more →When agents execute, the UX problem shifts from usability to trustability. Here are the four patterns that make delegation interfaces actually work.
Read more →How sports video intelligence evolved across three eras: from Hawk-Eye officiating to elite tracking data to AI-native single-camera analysis.
Read more →Software development has always been a workflow. As AI agents take on the work, the SDLC maps precisely to the agent architecture it helped inspire.
Read more →The dashboard was built for direct manipulation. As agents take on routine tasks and queries, it is becoming a secondary oversight surface.
Read more →Game state reconstruction is converging as the organising goal of sports AI, shifting evaluation from isolated models to system-level pipeline metrics.
Read more →Human memory is not a recording. It is a reconstruction shaped by emotion, identity, and context. That changes how we should build technology around it.
Read more →Monocular broadcast cameras create occlusion, blur, and resolution constraints that define the real engineering constraints of sports CV.
Read more →Football dominates sports CV research for structural reasons. Understanding why reveals what transfers to other sports and where the gaps remain.
Read more →We treat content publishing as a software engineering problem: git-driven, schema-validated, human-approved. Here is how the pipeline works and why it is designed the way it is.
Read more →The AI pipeline that converts football video into structured game state, from player detection and tracking through tactical reasoning and natural language queries.
Read more →From vector similarity to knowledge graphs: how retrieval evolved in AI-native systems, and why retrieval quality is now a core product dependency.
Read more →Agent workflows don't fit inside a request handler. They pause for humans, retry on failures, and branch across tools. Orchestration frameworks are the new application server.
Read more →We built Grain CLI to answer a question nobody could: how do you trace AI-generated code back to the conversation that created it? Here's what we found.
Read more →APIs were designed for developers reading documentation. Now their primary consumers are AI agents. That shift changes how you should design them.
Read more →The most important user of your software may no longer be a person. When agents become your primary operators, everything changes: API design, permissions, UX, and what it means for a product to be usable.
Read more →Most products calling themselves AI-native are really AI-enhanced: they've added a model to an unchanged architecture. The distinction isn't semantic. It determines whether your product survives the next platform shift.
Read more →AI-native software isn't SaaS plus a chatbot. It's a fundamentally different architecture built around three layers: record, context, and action. Most product teams haven't separated them yet.
Read more →The frameworks that built the last generation of SaaS products won't survive the AI shift. Here's why builder-led teams need to rethink product strategy from first principles.
Read more →Whether you're exploring AI integration, rethinking your product strategy, or need a builder in your corner — let's talk.
Start a conversation →