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AI that can add its own tools has appeared

• An open-source AI agent called GrimmBot was introduced, showing a mechanism where, when it hits a wall, it can make new Python tools by itself, test them, and then use them going forward [1].
• This indicates a shift away from “running only with the tools it was given at the start,” toward filling in what’s missing on its own.
• It also included mechanisms for memory to keep working, remembering what it plans to do later, and even staying on-screen for long stretches [1].
• The key point is that design is becoming real where the AI doesn’t just “think,” but expands its own working environment when needed. As a result, even tasks that would be hard to finish with pre-made tools alone are less likely to get stuck mid-way.

Expected decline in the cost of using big AI models

• Research firm Gartner projected that by 2030, the spending on large-scale AI at the level of one trillion users’ scale will drop by 90% or more [2].
• Contributing factors include improvements in machines and infrastructure, refinements in how AI is built, better ways to use AI tools, and broader adoption of dedicated hardware for AI [2].
• However, it was also noted that even if the cost per use falls, overall spending may not decrease much if the number of times AI is used keeps rising [2].
• In other words, what gets cheaper is the unit price to run AI, but a company’s total cost can still change depending on how it uses AI.

Efficiency improvements for AI running on your own devices are also progressing

• A technique called attn-rot was reported to have been added to llama.cpp as a way to make AI run more efficiently on local devices [3].
• The goal is to reduce the waste of what the AI needs to “remember” during conversation and boost speed significantly [3].
• The report suggests that substantial performance gains are possible without a major drop in quality [3].
• For personal use or small-scale operations, these improvements can be the difference between “slightly faster” and whether it’s actually usable in practice.

Discussions highlighted that AI safety can’t be solved by power alone

• In thinking about safe AI, there was an argument that simply increasing performance won’t resolve issues like lying outputs or problems where the AI can’t correct itself the way people expect [5].
• In that discussion, it was emphasized that it’s important for AI to properly connect with real-world events and be able to retrace its reasoning when needed [5].
• In another post, it was argued that if AI is to have its own way to pay, it needs an “money container” for AI [4].
• Together, both point to the idea that to use AI widely, you must consider not only whether the answers are good or bad, but also how it will operate and how it will be managed.

Practical tips that you can use right away are increasing

• Five writing frameworks were introduced for making text feel less “AI-like.” They were organized so they can be used for emails, blogs, and social posts [8].
• A workflow was also shared to create a ~20-minute process for making a 60-second video by combining tools that are mostly free, with concrete steps from script creation to voice and editing [7].
• A prompt collection was introduced to help create handwriting practice materials for children while keeping the visuals consistent, showing ways to mass-produce good-looking outputs [6].

When working with AI, it’s more important to ask “how much should we delegate” than “what can it do?”

• AI will keep getting smarter, but you’ll generally fail less if you use it only where it’s strong rather than handing everything over.
• For example, you’ll often feel the biggest impact by delegating the annoying early steps—like initial preparation before reasoning, making a rough draft of text, or building a video outline.

In choosing how to use AI, don’t focus only on cheapness—look for “less waste”

• Even if AI costs go down, if you use it more often, the overall burden can increase without you noticing [2].
• So a smarter approach is to shift the criteria from “because it’s usable” to whether it truly reduces time, and whether it’s worth people rethinking their workflow around.

To use AI with confidence, build a habit of verification

• AI can confidently produce answers—but it can still be wrong [5].
• That’s why, especially for important decisions, it helps to maintain a flow where you don’t take the AI’s answer at face value, re-check the rationale, and have humans do the final confirmation.

The average person is best served by “try small, then expand the useful scenarios”

• It’s easier to stick with AI by applying it to small everyday tasks rather than aiming immediately for complicated use cases.
• Start with low-impact situations—like refining writing, creating short videos, or rewriting explanations to be clearer—and your discomfort with AI is likely to shrink.
• If you think of AI less as a tool that replaces you and more as a tool to get back your time, it becomes easier to engage with positively.