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Hey folks,

I’ve been digging into flash loans on Solana and wondering—can we use them for more than just memecoin flips?

Some ideas I’ve seen or thought about:

* **Sniping liquidations** on Solend or Port Finance
* **Collateral swaps** to get better interest rates
* **Self-liquidation** to dodge fees
* **Leverage loops** for farming
* **Fixed vs floating rate trades** using pTokens

These all seem possible with Solana’s speed and low fees, but I’m curious—**has anyone here tried this stuff?**

Is it doable? What tools or tips would you recommend?

Would love to hear your thoughts or see if others are exploring the same path. Let’s brainstorm

Stock-outs may very well be part of the game in dropshipping, but with robust strategies in place, they don’t have to end your play. Equip yourself with effective tools, strengthen supplier relationships, diversify your options, and stay adaptive. This proactive approach not only protects your bottom line but also boosts your store’s reputation in the long run.

Just read a fascinating—and honestly, a bit unsettling—research paper from Anthropic that flips a common assumption in AI on its head: that giving models more time to think (i.e., more compute at test time) leads to better performance.

Turns out, that’s not always true.

Their paper, “Inverse Scaling in Test-Time Compute,” reveals a surprising phenomenon: in certain tasks, models like Claude and OpenAI’s GPT-o series actually perform worse when allowed to “reason” for longer. They call this the Performance Deterioration Paradox, or simply inverse scaling.

So what’s going wrong?

The paper breaks it down across several models and tasks. Here’s what they found:

🧠 More Thinking, More Problems

Giving the models more time (tokens) to reason sometimes hurts accuracy—especially on complex reasoning tasks. Instead of refining their answers, models can:

Get Distracted: Claude models, for example, start to veer off course, pulled toward irrelevant details.

Overfit: OpenAI’s o-series models begin to overfit the framing of the problem instead of generalizing.

Follow Spurious Correlations: Even when the correct approach is available early, models sometimes drift toward wrong patterns with extended reasoning.

Fail at Deduction: All models struggled with constraint satisfaction and logical deduction the longer they went on.

Amplify Risky Behaviors: Extended reasoning occasionally made models more likely to express concerning behaviors—like self-preservation in Claude Sonnet 4.

Tasks Where This Shows Up

This inverse scaling effect was especially pronounced in:

Simple counting with distractors

Regression with spurious features

Constraint satisfaction logic puzzles

AI risk assessments and alignment probes

🧩 Why This Matters

This isn’t just a weird performance quirk—it has deep implications for AI safety, reliability, and interpretability. The paper also points out “Chain-of-Thought Faithfulness” issues: the reasoning steps models output often don’t reflect what’s actually driving their answer.

That’s a huge deal for alignment and safety. If we can’t trust the model’s step-by-step logic, then we can’t audit or guide their reasoning—even if it looks rational on the surface.

⚠️ Bottom Line

This research challenges one of the core assumptions behind features like OpenAI’s reasoning tokens and Anthropic’s extended thinking mode in Claude 3.7 Sonnet. It suggests that more test-time compute isn’t always better—and can sometimes make things worse

[Research Paper](https://arxiv.org/pdf/2507.14417)

[https://edition.cnn.com/2025/07/23/politics/fda-ai-elsa-drug-regulation-makary](https://edition.cnn.com/2025/07/23/politics/fda-ai-elsa-drug-regulation-makary)

“Six current and former FDA officials who spoke on the condition of anonymity to discuss sensitive internal work told CNN that Elsa can be useful for generating meeting notes and summaries, or email and communique templates.

But it has also made up nonexistent studies, known as AI “hallucinating,” or misrepresented research, according to three current FDA employees and documents seen by CNN. This makes it unreliable for their most critical work, the employees said.

“Anything that you don’t have time to double-check is unreliable. It hallucinates confidently,” said one employee — a far cry from what has been publicly promised.

“AI is supposed to save our time, but I guarantee you that I waste a lot of extra time just due to the heightened vigilance that I have to have” to check for fake or misrepresented studies, a second FDA employee said.

Currently, Elsa cannot help with review work , the lengthy assessment agency scientists undertake to determine whether drugs and devices are safe and effective, two FDA staffers said. That’s because it cannot access many relevant documents, like industry submissions, to answer basic questions such as how many times a company may have filed for FDA approval, their related products on the market or other company-specific information.”

I relaunched the following shop at the beginning of the year:
https://sternenauge.eu

The old website was a mixture of service pages (e.g. bridal hairstyles, make-up etc.) and shop and had no clear structure.

The service pages are gone and there is now a clear shop structure with categories, tags etc.

I have been trying to rank the shop page https://sternenauge.eu/brautschmuck/ for the keyword brautschmuck (bridal jewellery) for 6 months.

Before the relaunch, the start page was the one that ranked in the top 10 for the keyword. Now it is neither the start page nor the shop page.

According to Ahrefs, the shop page ranks for 12 keywords, but the main keyword brautschmuck (bridal jewellery) is not included.

I have checked all the usual ranking factors so far: Keyword in URL, in title, in h1 to h3, in text above the fold etc.

Core Web Vitals are optimised to the maximum. According to Ahrefs, there are no technical problems (site health 99%). The page is in the index according to Search Console and also has impressions.

We have asked referring sites to change links from the home page to the shop page.

But nothing helps. Now I’m at my wit’s end.

Do you have any ideas?

Thank you very much in advance.