context
Beyond Market Intelligence keeps context in one place: 31 stories so far. The section currently leads with “Smarter Prompts, Lower Costs: Compress Without Losing Context”, “Explore papers by selecting any passage, formula, or figure for instant AI clarity.”, and “Unlock AI's true ROI by empowering teams, not just individuals”. Every prompt carries more than it needs. Reading interp papers often means juggling passages between tabs just to make sense of a single formula. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every context story on Beyond Market Intelligence, newest first.
Smarter Prompts, Lower Costs: Compress Without Losing Context
Every prompt carries more than it needs. Long instructions, retrieved documents, and chat history inflate token counts, slow responses, and bury what matters. Prompt compression trims that excess while preserving the core meaning. It is a practical answer to a growing cost problem, and it deserves attention. If you want to see how agents learn by editing context instead of weights, our piece on Agentic Context Learning pairs well with this discussion. The takeaway is simple: leaner inputs, sharper outputs.
Explore papers by selecting any passage, formula, or figure for instant AI clarity.
Reading interp papers often means juggling passages between tabs just to make sense of a single formula. This project cuts through that friction. The tool lets you select any passage, figure, or citation and get an explanation grounded in the full paper's context, no context-switching required. It's a practical, honest solution built on a real pain point. Running it on a personal API key is refreshingly humble.
Unlock AI's true ROI by empowering teams, not just individuals
Atlassian's research points to a truth most organizations haven't caught yet: AI delivers value at the team level, not just for individuals. Dr. Molly Sands and her team found that while 89% of executives see individual speed improving, only 6% can name clear ROI. That gap is telling. The teams that succeed share context, redesign workflows, and build a learning culture. It's a practical, human-centered approach. For more on questioning AI's limits, see "Talking to My AI Clone Taught Me to Question the Tech."
Measure AI ROI by mapping the customer journey before you build
Zillow's engineering chief brought a refreshing dose of discipline to VB Transform 2026: measure before you build. Toby Roberts and Glean's Arvind Jain showed that context, not data, is the real bottleneck in AI. Zillow's persistent context layer carries customers through a journey that spans months and multiple professionals. That's a hard problem, and they're solving it with practical architecture. For those digging into the technical weeds, our guide to distributed algorithms pairs well with this systems-first thinking.
Explore how GPT-2 maps tokens with an interactive visual graph.
There's a quiet thrill in tapping a single token and watching its nearest kin appear. This interactive map of GPT-2's token embedding space makes that possible, offering 32,070 alphabetic tokens to explore with a pinch or a tap. No forward pass, no context, just raw relationships laid bare. The t-SNE layout and minimum spanning tree ensure every line represents a genuine neighbourly bond. It's a hands-on way to grasp how the model organises language, and it works beautifully on mobile.
Before retrieval begins, a small loop refines your question
The small loop runs before retrieval, and that sequencing is the insight. Read the doc, ask what is missing, re-parse. It is a tight, deliberate cycle that treats question parsing as its own engineering problem, not a footnote to context building. We appreciate the restraint here. This is prompt engineering, then context engineering, then loop engineering, each step earning its place. For readers tracking how LLMs navigate structure, our related piece on paragraph structure in token space pairs well with this disciplined approach.
Five Assets to Define Before Scaling Your AI Workflows
Preparing your AI agents for more responsibility starts long before you hand over the work. This guide focuses on the practical assets that matter: defining recurring tasks, giving AI the right context, clarifying what quality looks like, and knowing where human judgment stays essential. It's a grounded, actionable read for anyone feeling the squeeze of scaling automation. If you're ready to move beyond theory, this is the kind of foundation that makes the leap feel less daunting.