I was playing with a PDF that prints a nice contents page but, oddly, the file itself has no outline. The trick is to pull the visual list back into a real hierarchy—first by running OCR on that page to grab the headings, then by scanning the rest of the document for matching strings and building a tree from the font sizes and indentation cues.
The second angle skips OCR entirely: you feed the whole PDF through a layout engine that spots repeated header patterns, then you stitch those pieces together into a virtual table of contents. Both routes give you a clean, searchable map where none existed before.
The part most people miss is the page‑alignment step. After you have the headings, you compare each heading’s text against every page’s first few lines, using fuzzy matching to pin the exact page number. That alignment lets a retrieval‑augmented generation system pull just the right section, instead of rummaging through the whole file.
Understanding ow LLMs interact with the world around them, from returning data to taking action The post Tool Calling, Explained: How AI Agents Decide What to Do Next appeared first on Towards Data Science.
AWS is rolling out two tools that quietly reshape how AI assistants handle code and data. Continuum runs behind the scenes, sniffing out vulnerabilities in your codebase, ranking them by risk, and then automatically patching the most critical ones. It’s like having a diligent reviewer that never sleeps, keeping the security holes from slipping through.
The second service, Context, builds a dynamic knowledge graph from your company’s own documents, databases, and APIs. By stitching together that internal map, AI agents suddenly have the right business context to answer questions or generate outputs that actually make sense for your organization, rather than guessing from generic internet data.
Together they address the twin pain points that have been surfacing—fast‑writing AI that still makes costly mistakes, and models that lack the nuanced understanding of a specific business. The result is a tighter loop where code is safer and AI responses are grounded in the reality of your own data.
Sam Altman took the stage at Stanford and, instead of rehashing the usual hype, he dug into the mechanics that got us here. He argued that a whole generation of researchers kept the field’s momentum low because they didn’t fully appreciate how far scaling alone could push language models. The point wasn’t about bigger models for the sake of size; it was about the emergent capabilities that appear once you crank up the compute and data.
He illustrated his claim with a recent OpenAI experiment that knocked down a long‑standing mathematical conjecture. The model, when given enough parameters and training data, found a proof that had stumped specialists for years. That single result, he said, shows how scaling can unlock problem‑solving that we previously thought required entirely new architectures.
Altman’s take is a reminder that the “slow‑and‑steady” mindset may have been a bottleneck. By underestimating the raw power of scale, many researchers inadvertently set a ceiling on what AI could achieve. The lesson, according to him, is to let the hardware and data do the heavy lifting and let the models discover the surprising shortcuts.
So, in a nutshell, the conversation shifted from “what new tricks do we need?” to “what happens when we just let the models grow big enough?” The answer, at least for now, is that scaling can surprise us in ways we didn’t think were even on the table.
I dug into the Berkeley data set—over half a million grades, spanning a bunch of majors. What caught my eye was a subtle bump in scores for courses that lean heavily on writing or code, and the timing lines up almost exactly with ChatGPT’s public debut.
The rise isn’t uniform across the board; it’s most pronounced in the homework slots rather than exams or projects. That pattern suggests the extra points are coming from something that can be outsourced, not from a deeper grasp of the material.
If you think about it, a language model can draft essays, debug snippets, and even generate plausible code solutions. When students feed that into their assignments, the grading algorithms—still largely based on correctness—reward the output, inflating the marks.
The takeaway feels less like a learning boost and more like a signal: AI tools are being used to do the work for students, and the grading systems haven’t caught up to differentiate between original effort and assisted output.
I’ve been thinking about the way we usually cobble together date tables with DAX when the source data doesn’t give us one, and it turns out there’s a whole toolbox we’ve been overlooking. The first option is to lean on Power Query: you can generate a calendar by adding a custom function that steps through a start and end date, then expand it into a full table. It’s quick, keeps everything in the same ETL flow, and you get all the typical columns—year, quarter, month name—without writing a lot of DAX.
The second route is to pull a ready‑made date dimension from the data warehouse, if one exists. That shifts the maintenance burden upstream and guarantees consistency across reports, but it does mean you’re dependent on the data model team to keep it up to date. Some folks also use third‑party connectors that expose a calendar as a separate query, which can be handy when you need a shared reference across multiple Power BI files.
Finally, there’s the hybrid approach: generate a minimal skeleton in Power Query, then enrich it with DAX calculated columns for things like fiscal periods or custom holidays. It gives you the flexibility of on‑the‑fly adjustments while still anchoring the core dates in the query stage. In practice, I’ve found the hybrid method strikes the best balance—fast enough for ad‑hoc analysis, but robust enough to stay aligned when the model scales.
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