#023 • 3 June 2025

Contents of this Newsletter

👔

Smart Context

⏰ : 6 min

📓

Blog Picks

⏰ : 9 min

🗑️

AI Slop

⏰ : 4 min

🗞️

Weekly AI News

⏰ : 6 min

MAIN STORY

The Craving For Greater Context Models

Claude 4 dropped, benchmarks soared, and the one thing everyone wanted more of? Context. Not faster models. Not more tools. Just bigger windows.

Alex Albert

@alexalbert__

It's been a week since Claude 4 launch. Tell me everything you like and don't like about the new models so we can keep pushing on it or fix it in the future!

8:30 PM • May 29, 2025

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But here’s the catch: context isn't memory and this is a common misconception. In large language models (LLMs), context is session-based. It's the stuff you load into the window at runtime. Memory, on the other hand, is persistent. It lets models "remember" things across sessions.

Claude 4 can handle 200K tokens of context, which is pretty good. But once the window closes, it forgets. That brilliant reply you got? It has no idea that it is responsible for it.

r/ClaudeAI

Posted by u/serg33v • 12 days ago

Claude 4 and still 200k context size

I like Claude 3.7 a lot, but context size was the only downsize. Well, looks like we need to wait one more year for 1M context model.

Even 400K will be a massive improvement! Why 200k?

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This distinction matters, especially for agent workflows. Memory builds continuity. Context builds coherence.

Why Context Is Expensive

Let’s talk about what expensive really means. Larger context windows slow things down because the model has to compare every token to every other token, making processing time grow rapidly as input size increases. At the infrastructural level, transformers scale poorly. More tokens mean more GPU time, more memory, and a bigger bill.

Then there’s the human cost. Loading more context means curating more context. Someone has to decide what goes in and what gets cut because without good filtering, long prompts just add noise. And noisy prompts aren’t just inefficient, they’re dangerous: they confuse the model and tank output quality.

Finally, there’s the architectural cost. You can’t just “go bigger.” You need systems that chunk intelligently, retrieve selectively, and reason about what’s actually useful. Otherwise, you’re just stuffing a smart model with dumb input.

Claude 4 might feel amazing, but under the hood, context expansion means engineering pain: context fragmentation, slowdown, and degraded quality if you stuff too much.

Hardware Is Catching Up

To scale context, hardware’s evolving. Three breakthroughs matter:

KV Caching: Instead of recalculating attention on the entire prompt, models cache past tokens (Keys and Values), speeding up inference.
Unified Memory (NVL72): NVIDIA’s Blackwell chips blur the line between GPU and system memory. Less shuffling = more space for context.
Parallel Attention (like NSA / Squid): Innovations here reduce attention bottlenecks by letting attention ops run concurrently or reuse computation.

These make long contexts feasible, but not cheap.

Insights from the Cursor Research Roundtable

A recent roundtable discussion by some of the founders and researchers at Cursor, offered some sharp insights into the evolving best practices for context handling in coding agents:

Chunking Is Foundational: Breaking tasks into smaller, coherent units improves prediction quality and makes context manageable.
Dynamic Context Curation: More context isn’t better if it’s noisy. Filtering based on relevance and using feedback from what users actually accept helps tune prompts for clarity.
From Benchmarks to Behavior: The most meaningful signals come from real-world feedback loops replacing artificial test-based rewards.
The Context Revolution: Thanks to hardware advances, we’re nearing a point where entire codebases (or end-to-end test sessions) can be loaded in one window.
Infrastructure Is the Moat: The edge is better infrastructure for handling massive context, real-time learning loops, and efficient retrieval.

These themes hit especially hard in QA workflows. Context here isn’t just code, it’s logs, UI state, device behavior, and user input. Where code generation benefits from structure, testing demands precision despite noise.

Solving the Context Problem At Quash

Coding is context-hungry. Every autocomplete, every Copilot suggestion, it's all dependent on the right context being loaded at the right time. But if coding is hungry for context, then testing is starving for context.

Unlike code generation, where you might control the structure, testing needs to understand what happened. It has to trace steps across files, logs, device states, UI layers, and user behavior and this type of data is scattered, noisy, and incomplete.

So while most dev tools fight for more tokens, testing platforms have to fight for smarter ones.

We handle this in three ways:

Smart Chunking, Not Dumb Splitting

We don’t just split inputs by character count or fixed rules. Our agents chunk context semantically, preserving meaning across segments. That means no broken stack traces, no cut-off test steps.

Language-Aware Segmentation

Markdown, Stack traces, Code diffs—we handle them differently. Each input type has its own compression and formatting logic. Screenshots go through OCR + VQA pipelines. Code gets AST-based splitting. Natural language is grouped by conversational coherence.

Agentic RAG

Our agents don’t retrieve blindly. They reason about what context is needed before pulling it in. This saves tokens and improves answer relevance. Think of it as pre-prompt planning, not just stuffing.

Together, these help us operate well within smaller context windows and only go long when we actually need to.

The Age of Smart Context

We’re entering an era where the best models don’t just remember more, they remember better. Claude 4 shows that context isn’t just about window size anymore. It’s about judgment. Filtering. Pattern recognition. And that’s where product design comes in.

At Quash, we’re not chasing “context max.” We’re building a context that’s selective on purpose—chunked by meaning, aware of structure, optimized for what testing actually needs. Because in practice, 200K tokens don’t help if the model grabs the wrong 200K.

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AI Slop in American Political Movements

The Make America Healthy Again (MAHA) report, released last week by Robert F. Kennedy Jr.’s commission under the Trump administration, claimed to cite hundreds of studies backing its health claims. But many of those citations simply don’t exist or are wildly misrepresented.

For example, the report cited a study on adolescent anxiety attributed to Columbia epidemiologist Dr. Katherine Keyes—who confirmed she never authored such a paper. Another referenced article supposedly by psychiatry chair Dr. Robert Findling was also denied and subsequently removed from the updated report. Several other studies cited under journals like Pediatrics have no record of existing in the form listed.

FactPost

@factpostnews

RFK Jr.'s 'MAHA Report' includes at least 7 cited sources that do not appear to exist. One scientist stated that "the paper cited is not a real paper that I or my colleagues were involved with," when informed of the report.

7:14 PM • May 29, 2025

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Some citations contain broken or missing DOIs (Digital Object Identifiers), incorrect authorship, or misattributed journals. White House press secretary Karoline Leavitt called these issues “formatting errors” to be fixed in an updated report, an excuse that tries to overlook the real problem.

Imagine this kind of AI slop influencing a major political health initiative in one of the world’s leading nations. The stakes couldn’t be higher. Experts speculate that these citation errors likely stem from unvetted AI-generated content, a warning sign familiar to anyone who has been using AI for years. We test software for a living, and we wouldn't even ship something with this many hallucinated footnotes.

When trusted health policies rest on shaky evidence, public trust suffers and that’s a failure no amount of “formatting fixes” can cover.

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This Week in AI

OpenAI Plans to Transform ChatGPT into an AI ‘Super Assistant’

According to a 2024 internal strategy document revealed during the US Department Of Justice’s Google antitrust case, OpenAI aims to evolve ChatGPT into an all-in-one “AI super assistant” by early 2025. This assistant will deeply understand users and serve as their main interface to the internet and daily life—helping across home, work, and on-the-go scenarios.

DeepSeek Releases Updated R1 AI Model on Hugging Face

Chinese startup DeepSeek has released a minor update to its powerful 685B-parameter R1 reasoning model on Hugging Face under the MIT license, allowing commercial use. While details are sparse, the release includes configuration files and model weights. Too large for most consumer hardware, R1 has drawn attention for rivaling OpenAI’s models—and concern from U.S. regulators over national security implications.

ElevenLabs Adds Text + Voice to Conversational AI

ElevenLabs has launched a major upgrade to its Conversational AI platform: true multimodal support for both voice and text inputs in real-time. This hybrid setup improves user experience by allowing seamless switching between speaking and typing which is especially useful for precise data like emails or IDs that are often misheard.
Why it matters:

Higher accuracy: Users can type tricky inputs instead of relying on voice alone.
Better UX: Reduces frustration with complex or sensitive data.
Smoother interactions: Mimics natural conversations with fluid voice-text transitions.

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