Likewise, major technical and security incidents increasing 40% is a loss, and the amount of time employees spent firefighting them rising 70% is a loss.
> Why is PR count or number of issues relevant for the overall outcome?
Agree on PR count but how is number of issues not relevant? If throughout increases 36% but incidents also rise by 40% that’s pretty clearly a net negative IMO.
This is so right. We training Whisper Large model on 20,000 audio samples specific to a domain and it ended up reducing the ASR by 5% while improving WER of the finetuned domain by 0.5%.
Instead we ended up with no finetuning. We give audio snippet to 2 AsR models, take 3 best transcriptions and ask the LLm to pick the best based on the context. That produced significantly higher accuracy in how an agent understands the users.
basically he is feeding the same input to multiple models, taking their outputs and dumping it into an LLM to sort out what the actual transcription probably is. expensive but effective.
Deep Fusion is best, when words and phrase patterns in the domain are known. Deep Fusion means to hint the Whisper decoder about the next possible words using LLM-in-the-loop.
- Work is shifting from building/doing to evaluating, judging, and steering — that's where human value will concentrate.
Other supporting points.
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- No lab milestone or "RSI breakthrough" will suddenly eliminate jobs — economic impact unfolds gradually over decades.
- Reliability, not raw capability, is the real bottleneck holding back AI automation today.
- Historically, making work cheaper/faster (ATMs, radiology, coding) has grown employment, not destroyed it.
- Superintelligence claims misunderstand human intelligence, which is itself amplified by tools like AI ("co-superintelligence").
It is not a good idea to compress articles like this but there are many of these opinions to read and trying to get to the point quickly to uncover new viewpoints.
1. It follows that all of human authorship is pointless since search engines now actively bury content and impede access to written documents. Long Live The New Flesh.
2. Webrings are obviously an artifact of people who hate AI slop and yearn for the simpler days of the older Web, before search engines were effective. Therefore the remark about generated summaries is powerfully sarcastic, as the twain shall never meet. Moreover, Google is no longer fit for purpose, so webrings are an apposite solution to the empty niche of trustworthy discovery mechanisms. Eat The Rich.
Which one the author actually meant depends entirely on their literacy regarding the web revival movement.
Thank you! Showing the data in a web page should definitely be possible. But I’m not sure if this matches the original idea I had, where the tool would run in the terminal only. Why do you feel the need to show the data in a web page? Is there anything missing in the CLI?
The problem with articles like this one is, they give ways to become efficient at handling more addiction, at the individual level. Nothing for others part of this, companies developing the software and organizations employing these tools.
Summary of the addiction management tips from the article.
1. Time-box your AI coding sessions with a clear goal and a hard end time.
2. Separate exploration (testing ideas) from execution (shipping code) to avoid losing focus.
3. Prioritize sleep, hard stops, and actual recovery as essential maintenance, not just wellness.
4. Invest in structured training to move from basic usage to advanced multi-agent workflows.
5. Personalize your AI workflow to fit your needs while actively avoiding common anti-patterns.
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When a developer stops writing code and starts using Claude to handle multiple projects at once, they are essentially managing the outcomes.
They have become 10x engineering managers. The context strain and emotional strain is overwhelming.
> Nothing for others part of this, companies developing the software and organizations employing these tools.
lack of systems-thinking and over focus on the individual has been a trend since a long time in business. its all on the individual worker to fix things for themselves...
The software world is very close to building a super intelligent senior software developer. Companies like this will ask all the best things a software engineer does automatically. Now claude will add it into the coding agents itself.
Damn, I didn't see this coming.
Its first the build the intelligent builder. We will figure out what we want to build later.
Edit: Before more people take it seriously. This is sarcasm. I don't wish this.
Once the automator automates itself fast enough, we won't have the ability to opine what gets built. The LLM will decide. Just like right now sometimes LLMs delete tests so they pass, they could just delete humanity if humans get in their way.
It's the error rate. That's what everyone found when they were trying to go Full Auto with OpenClaw in February.
You can rely on it like 95% of the time but that means if you keep it running continuously the error rate rapidly approaches 100%. That's getting a little better with each release, and it might actually hit the point where you can more or less trust it indefinitely (on well defined workflows).
Or at least it would, if context window permitted...
> The software world is very close to building a super intelligent senior software developer. Companies like this will ask all the best things a software engineer does automatically. Now claude will add it into the coding agents itself.
Except Claude is more expensive than an actual senior software developer. Otherwise, why are many companies terrified of the usage bill that gets printed on the invoice?
The nonsense in "tokenmaxxing" was a complete marketing scam and illusion of cheap tokens which in reality were heavily subsidized.
The entire point is detecting bad code before it reaches production. [0] AI generated or not.
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