Measuring Product Impact When A/B Testing Is Not Available
How to evaluate product releases without an A/B test. A trustworthy framework using causal inference, Synthetic Control, and rigorous data guardrails.
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February 2026
Generative Audio and the Evolution of Cinematic Sound Design
AI-driven generative audio is transforming film sound design, from Foley stages to diffusion models, enhancing creativity and cinematic storytelling.
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The Three Questions Every Startup Should Ask Before Building AI
• Not all hard problems need ML—validate that adaptive learning actually solves your business problem before building. • Start with simple models (logistic regression, XGBoost) over cutting-edge architectures; prove concept viability first. • Data infrastructure matters as much as the model; invest in sourcing, labeling, and validation before training. • Build feedback loops and track real-world metrics from day one; plan for retraining and model iteration. • Calculate unit economics upfront—inference, training, and infrastructure costs must align with […]
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When Your Metrics Lie: The Illusion of Observability
Green dashboards don’t mean healthy users. Most teams monitor infrastructure (CPU, memory, disk) instead of outcomes (checkout success, error rates, p99 latency). The fix: define 2–3 SLIs tied to what users actually do, set SLOs on them, alert on error budget burn rate — not infra blips. Audit your alerts, add synthetic monitoring on critical user flows, and ask customer success what broke before engineering noticed. Everything else is noise.
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EvolveGen: Algorithmic Level Hardware Model Checking Benchmark Generation through Reinforcement Learning
Progress in hardware model checking depends critically on high-quality benchmarks. However, the community faces a significant benchmark gap: existing suites are limited in number, often distributed only in representations such as BTOR2 without access to the originating register-transfer-level (RTL) designs, and biased toward extreme difficulty where instances are either trivial or intractable. These limitations hinder rigorous evaluation of new verification techniques and encourage overfitting of solver heuristics to a narrow set of problems. To address this, we introduce […]
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I Was Ready to Return My DGX Spark. Then NVIDIA’s January Update Changed Everything.
I almost returned the $4,000 DGX Spark. Then NVIDIA dropped 30 playbooks, 2.5x performance gains, and hybrid routing.
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Google API Keys Weren’t Secrets. But then Gemini Changed the Rules.
Google API Keys Weren’t Secrets. But then Gemini Changed the Rules. Yikes! It turns out Gemini and Google Maps (and other services) share the same API keys… but Google Maps API keys are designed to be public, since they are embedded directly in web pages. Gemini API keys can be used to access private files and make billable API requests, so they absolutely should not be shared. If you don’t understand this it’s very easy to accidentally enable […]
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$φ$-DPO: Fairness Direct Preference Optimization Approach to Continual Learning in Large Multimodal Models
Fairness in Continual Learning for Large Multimodal Models (LMMs) is an emerging yet underexplored challenge, particularly in the presence of imbalanced data distributions that can lead to biased model updates and suboptimal performance across tasks. While recent continual learning studies have made progress in addressing catastrophic forgetting, the problem of fairness caused the imbalanced data remains largely underexplored. This paper presents a novel Fairness Direct Preference Optimization (FaiDPO or $φ$-DPO) framework for continual learning in LMMs. In particular, […]
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[D] Evaluating the inference efficiency of Sparse+Linear Hybrid Architectures (MiniCPM-SALA)
We’ve seen a lot of talk about Hybrid models lately (like Jamba). I just noticed that OpenBMB and NVIDIA are running a performance sprint (SOAR 2026) specifically to benchmark MiniCPM-SALA (Sparse+Linear) on SGLang. The challenge is to optimize sparse operator fusion and KV-cache efficiency for ultra-long context. Since the leaderboard just opened today, I was wondering: from a systems research perspective, do you think this hybrid approach will eventually surpass standard Transformers for inference throughput in production? Has […]
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When Cloud Bills Crash the System: Cost as a Reliability Issue
Cloud cost and system reliability are the same problem viewed through different instruments. Cost anomalies surface bugs, retry storms, and memory leaks before they cause outages — if you’re watching. The fix: embed billing telemetry into your observability stack, enforce resource tagging at the pipeline level, write anomaly-based cost alerts, and treat budget overruns as budget burns the same way you treat SLO violations. Manage them separately and you’ll always be six weeks behind the failure that caused […]
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