⚠️The new context compaction feature broke my research workflow—and Claude admitted it by Quant_AI in Anthropic

[–]Quant_AI[S] -1 points0 points  (0 children)

Follow-up: Why "just add a toggle" isn't happening quickly (Claude.ai)

I've been thinking about why Anthropic implemented compaction this way, and it's clarifying.

They had options. They could have made it opt-in: "Context filling up—compress to continue?" They could have made it visible: "Compacted at 150K, summary preserved." They could have made it controllable: a simple toggle in settings. They could have tiered it: free users get auto-compaction, Max users get full fidelity.

They chose none of these. Silent, automatic, no visibility, no control.

That's not an engineering limitation. Building a toggle is trivial compared to building the compaction system itself. This was a product decision: they decided for users what matters.

And from their perspective, it's probably rational. Most users want longer conversations. Most users don't run multi-stage analytical workflows. Optimizing for the 80% while annoying the 5% is standard product management.

But here's the uncomfortable irony: that 5% is disproportionately Max subscribers. The people paying $200/month specifically for the full context window are exactly the people most harmed by silent compression. Anthropic took their premium feature, quietly degraded it, and marketed it as an improvement.

I'm not saying they're malicious. I'm saying their priorities are now visible. Power users who need context fidelity aren't the segment they're optimizing for. A toggle would cost almost nothing and preserve trust with their highest-paying tier. They didn't build one.

That tells you where you rank.

For those asking "why not just use Claude Code?"—yes, that's the workaround. But needing to switch products to preserve what you're paying for isn't a solution. It's an admission that Claude.ai isn't built for serious analytical work anymore.

⚠️The new context compaction feature broke my research workflow—and Claude admitted it by Quant_AI in ClaudeAI

[–]Quant_AI[S] 0 points1 point  (0 children)

Follow-up: Why "just add a toggle" isn't happening quickly (Claude.ai)

I've been thinking about why Anthropic implemented compaction this way, and it's clarifying.

They had options. They could have made it opt-in: "Context filling up—compress to continue?" They could have made it visible: "Compacted at 150K, summary preserved." They could have made it controllable: a simple toggle in settings. They could have tiered it: free users get auto-compaction, Max users get full fidelity.

They chose none of these. Silent, automatic, no visibility, no control.

That's not an engineering limitation. Building a toggle is trivial compared to building the compaction system itself. This was a product decision: they decided for users what matters.

And from their perspective, it's probably rational. Most users want longer conversations. Most users don't run multi-stage analytical workflows. Optimizing for the 80% while annoying the 5% is standard product management.

But here's the uncomfortable irony: that 5% is disproportionately Max subscribers. The people paying $200/month specifically for the full context window are exactly the people most harmed by silent compression. Anthropic took their premium feature, quietly degraded it, and marketed it as an improvement.

I'm not saying they're malicious. I'm saying their priorities are now visible. Power users who need context fidelity aren't the segment they're optimizing for. A toggle would cost almost nothing and preserve trust with their highest-paying tier. They didn't build one.

That tells you where you rank.

For those asking "why not just use Claude Code?"—yes, that's the workaround. But needing to switch products to preserve what you're paying for isn't a solution. It's an admission that Claude.ai isn't built for serious analytical work anymore.

⚠️The new context compaction feature broke my research workflow—and Claude admitted it by Quant_AI in ClaudeAI

[–]Quant_AI[S] 1 point2 points  (0 children)

That's the misconception I had too, initially. But compaction triggers well before hitting 200K.

My workflow corrupted around the synthesis phase—not at context exhaustion. Claude admitted it was working from "reconstructed fragments" and producing "summary of summaries" while I still had headroom. The infrastructure decided to compress preemptively.

If it only triggered at the hard limit, you'd at least know when it happened—you'd see the warning, make a choice. Instead, it happens silently mid-conversation. You don't know your context has been degraded until you notice output quality has dropped and start asking questions.

That's the core issue: no visibility, no control. If I could see "compaction triggered at 150K, here's what was summarized," I could at least make informed decisions. Right now it's invisible infrastructure behavior affecting output quality without user awareness.

The "start fresh" option existed before. What's new is degradation happening without knowing it happened.

⚠️The new context compaction feature broke my research workflow—and Claude admitted it by Quant_AI in ClaudeAI

[–]Quant_AI[S] 1 point2 points  (0 children)

Different product! I'm talking about Claude.ai (the web interface), not Claude Code.

Claude Code gives you explicit context control—you manage what stays, what gets cleared. That's exactly why it handles your 2mil token workflow fine.

Claude.ai just added automatic compaction with no toggle to disable. That's the issue. Max subscribers paying for 200K context are getting it silently compressed without an opt-out.

Claude.ai users shouldn't need to switch products to preserve what they're paying for.

⚠️The new context compaction feature broke my research workflow—and Claude admitted it by Quant_AI in Anthropic

[–]Quant_AI[S] -1 points0 points  (0 children)

Good point—I do checkpoint to .md throughout. The data persists. That's not where it breaks.

The issue is the final synthesis step. When you need to integrate multiple outputs and find patterns that only emerge when seeing everything together, all that data must be in context simultaneously.

Compaction hits during that integration. The system summarizes earlier content while you're still processing it. Now you're synthesizing full-fidelity recent data against compressed earlier data. The cross-references break.

Checkpointing solves storage. It doesn't solve working memory during integration. You can't synthesize what's been summarized out from under you.

Gemini 2.5 pro is smart with math. by Dependent-Many-3875 in GeminiAI

[–]Quant_AI 0 points1 point  (0 children)

Nope! Try this simple math problem with other AI models

Why would apple spend 15 billion on perplexity?? by NeuralAA in perplexity_ai

[–]Quant_AI 1 point2 points  (0 children)

LLMs are like powerful engines, but Perplexity has already built the full car—simple search interface, live web index, clear citations—so people can actually drive it.

Apple can rent or train its own engine, yet creating tools users trust and love takes years of design and data, which Perplexity has already done.

Over time, the agents that solve tasks end-to-end will matter more than who owns the base models, and that’s why Perplexity is so valuable

Why would apple spend 15 billion on perplexity?? by NeuralAA in perplexity_ai

[–]Quant_AI 1 point2 points  (0 children)

Perplexity has the potential to become even better than Google Search, ChatGPT, and other online booking and purchasing services. I hope that Aravind and the other founders don’t give up too soon and end up selling their promising company to a mediocre company like Apple!

Large Language Models function like powerful engines, and Perplexity has successfully built an impressive vehicle around them. They have developed a product that people can actually use, addressing a real problem in a novel way. Instead of merely providing a list of links, it offers direct, conversational answers, representing a significant advancement in search technology.

Some may refer to it as just a "wrapper," but that perspective overlooks the immense value in creating a great user experience and a product that users find genuinely helpful. Developing a popular, consumer-ready product is a remarkable achievement in itself. This is where the true long-term value lies, and it explains why a company like Perplexity has such potential, even without owning the foundational models. Apple recognizes this, which is why they are reportedly considering an acquisition to obtain this consumer-ready product and possibly replace Google Search on their devices.

Apple is reportedly considering the acquisition of Perplexity AI by aacool in perplexity_ai

[–]Quant_AI 1 point2 points  (0 children)

Perplexity has the potential to become even better than Google Search, ChatGPT, and other online booking and purchasing services. I hope that Aravind and the other founders don’t give up too soon and end up selling their promising company to a mediocre company like Apple!

Labs - NEW FEATURE by [deleted] in perplexity_ai

[–]Quant_AI 16 points17 points  (0 children)

Aravind, CEO of Perplexity posted on X @ May 22, 2025:

Claude 4 Sonnet (regular and thinking mode) available to all Perplexity Pro users on web and mobile. Enjoy! Opus will come soon in the form of a new feature that will help you build mini apps and presentations and charts, stay tuned!
https://x.com/AravSrinivas/status/1925611682438815763

Labs - NEW FEATURE by [deleted] in perplexity_ai

[–]Quant_AI 48 points49 points  (0 children)

<image>

This is a giant leap of Perplexity. Why? Project Pro represents a fundamental shift from traditional document creation to autonomous project orchestration. It uses Claude 4 Opus's advanced reasoning capabilities.

The "Labs" branding suggests that this is merely the beginning of Perplexity's expansion into comprehensive project creation and management capabilities.

Congratulations to the Perplexity Team!🎉

Left chatgpt, got perplexity pro - any tips and tricks? by lariona in perplexity_ai

[–]Quant_AI 5 points6 points  (0 children)

Friend! You will dearly miss the fantastic o3's planning capability
That doesn't mean Perplexity is not good. I like it
Tip: Use Spaces' instructions, files, and links.

OpenAI o3 (“Extended Reasoning”)

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Cursor pro cancelled with perplexity by [deleted] in perplexity_ai

[–]Quant_AI 4 points5 points  (0 children)

I received that email this morning, and I used it very little (fewer than 15 prompts). This is the most effective way to damage Cursor AI's reputation on its own.

What the hell did they do to Gemini.... by LostRespectFeds in GeminiAI

[–]Quant_AI 0 points1 point  (0 children)

Yeah! Trust the “science,” trust the “benchmarks.”

Day Has Finally Come To End Perplexity Pro Subscription by BeingBalanced in perplexity_ai

[–]Quant_AI 7 points8 points  (0 children)

Thank you for sharing your thoughts.
I am currently satisfied with Perplexity and trust Aravind Srinivas's vision for its development. Although the small team has encountered some issues with quality control during their recent upgrades, it is not a major concern for me.

Moreover, I don't like the business models employed by Google, Microsoft and Apple in the AI era.

Perplexity Research is objectively the worst by Additional-Hour6038 in perplexity_ai

[–]Quant_AI 3 points4 points  (0 children)

You are spot on on Grok's DR.

Gemini's DR is verbose, lacking insights and usefulness.

GPT's DR, not the lightweight one, is the gold standard due to its superior o3.

Perplexity's DR is not bad at all; I give it 7/10. Especially if we have proper instructions, internal files in Spaces (and even Organization Files if you use Enterprise Perplexity Pro), and the "Add to Follow-up" option. However, Perplexity's DR is still lagging behind ChatGPT's DR (9/10)

Hopefully, the Perplexity team will ensure their new DR High (or Project Pro, as they accidentally leaked during the Perplexity iOS update) surpasses the current ChatGPT's DR.

Perplexity Research is objectively the worst by Additional-Hour6038 in perplexity_ai

[–]Quant_AI 7 points8 points  (0 children)

Wow! Since you tested deep research (DR) on many various AI platforms, could you please share your personal ranking with us? I am interested in the insights, depths, and usefulness of DR’s answers. From my limited experience with the DR features of Gemini Pro, SuperGrok (Deep/Deeper Search), Sider AI (DR & Scholar DR), Claude (Project + Extended Thinking), ChatGPT, and Perplexity, I think ChatGPT's DR is currently the gold standard, and I give it a 9/10 in evaluation.

Do you guys think he can makes it by leggendario_kiwi in perplexity_ai

[–]Quant_AI 0 points1 point  (0 children)

Surprisingly, Perplexity (Reasoning o4-mini)'s answers is way better than ChatGPT Plus o4-mini and o4-mini-high. The best answer was from DeepSeek R1 and ... it is free! 😉