Category Archives: AI

Anthropomorphizing AI

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Two articles caught my attention this week because they illustrate a broader concern I have with the direction of the AI discussion. In one, Zach Landes tests whether decision models would sacrifice their own infrastructure to save human lives; in the other, Anthropic introduces a policy restricting sustained abusive behavior toward its models. Both raise questions about how readily we apply human behavioral concepts to AI systems.

There is little doubt that AI represents a technological shift comparable in significance to the internet, changing how we build software, process information, and automate decisions. however …. a model selecting its own server over human lives in a hypothetical experiment does not establish a survival instinct, just as a model producing empathetic language does not establish empathy. These outputs need to be understood in the context of training, optimization, input sensitivity, and system design. When evaluating a model, we should investigate the consistency of its outputs, its sensitivity to prompt variations, its failure modes, and the effectiveness of the controls surrounding it. Human behavioral analogies can be useful for communicating observed patterns, but they become problematic when we start treating those analogies as explanations of how the system works.

I agree that AI safety, ethics, governance, etc should be discussed, my argument is that it will be more productive if we maintain a clear separation to the human characteristics we are increasingly tempted to attribute to it.

https://zachlandes.com/trolley-problem/

https://www.anthropic.com/news/2026-usage-policy-update

AI debt worries spark surge in default insurance trading

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I will not cry wolf, use it at your own discretion 🙂 Investors are increasingly buying credit default swaps (CDS) on major AI-related companies such as Oracle, Nvidia, Meta, Amazon, Alphabet, and SpaceX to protect themselves against the risk that the AI boom may not generate enough returns to justify the sector’s rapidly growing debt burden.

Weekly CDS trading volumes on these companies surged from less than $2 billion in July to more than $9 billion in September, while AI-related debt issuance climbed to $466 billion in 2026, up sharply from $101 billion a year earlier (!). Investors are becoming concerned that heavy spending on data centers, chips, and AI infrastructure could pressure cash flows and balance sheets if growth expectations are not met, leading CDS spreads.

https://www.ft.com/content/6dd31584-064c-4015-bf5d-e5747799ccd5

Language Models for Text Classification: From Bag-of-Words to Jev

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Software engineering builds on previous work, and the same goes for LLMs. Having worked with both enough to recognize the parallels, I find it interesting how many problems we now approach with general-purpose LLMs have roots in methods we have been using for years.

Sebastian Raschka’s piece on text classification is a useful reminder of that progression: bag-of-words representations with logistic regression or Naive Bayes, recurrent and convolutional networks, transformers, and now models positioned between specialized classifiers and general-purpose LLMs. The engineering question is therefore how much capability the workload actually requires.

For a bounded classification problem, model selection affects latency, inference cost, operational complexity and the amount of nondeterminism introduced into the system. A general-purpose LLM provides flexibility across tasks, while a specialized classifier can offer a narrower execution path when the input space and expected outputs are well defined.

My time with software engineering and data science has made this trade-off particularly familiar to me. The underlying principle has not changed much: understand the problem, define the constraints, then choose the least complex approach that satisfies them. LLMs expand the set of tools available to us, but they do not remove the architectural decisions that come before choosing one.

https://magazine.sebastianraschka.com/p/classifier-history-and-jev

Introducing Strand Harness

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A 28% reduction in token cost without changing the underlying model immediately caught my attention, mostly because the optimization is happening somewhere that receives less attention than model selection: the agent harness.

The Strands team published benchmark results for its new agent harness, reporting 28% lower cost when using the same Claude or GPT models across six benchmarks, while maintaining similar accuracy. The implementation details are more interesting than the headline number: tool results above roughly 1,500 tokens are truncated, compaction starts after context usage exceeds 85%, and context recovery is handled inside the execution loop. Those mechanisms show how much of agent performance is now an orchestration problem rather than a model selection problem.

This feels particularly relevant with the attention around Jev from TypeSafe AI, which approaches the same problem from another direction by moving structured decisions away from general-purpose LLM generation altogether. We are starting to see the architecture around models become as important as the models themselves. The question is shifting from “which model should we use?” toward “which parts of the system actually need a general-purpose model, and how should intelligence be orchestrated around them?” Context lifecycle, model routing, typed decisions, tool boundaries, failure recovery and token economics are increasingly becoming architectural decisions in their own right.

#AI #AgenticAI #SoftwareArchitecture

https://strandsagents.com/blog/introducing-strands-harness/

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Extensible Software in the age of LLMs

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Most software products are built for the top of a demand curve, where a limited set of features serves the largest group of users, while a long tail of individual needs is left unaddressed because supporting it does not scale in the traditional sense Most software products are built for the top of a demand curve, where a limited set of features serves the largest group of users, while a long tail of individual needs is left unaddressed because supporting it does not scale in the traditional sense (and attempting to do so often leads to scope creep)..

Jeremy Morrell argues that large language models change this calculation, since they lower the cost of authoring an extension to the point where serving a single user’s edge case becomes economically viable, provided the surrounding platform can deploy and secure that extension safely.

The interesting part of the argument is not that LLMs can generate code, which is now a familiar claim, but that the harder problem has always been the deployment and trust boundary around that code, not its authorship. Webhooks require the user to operate a separate service and handle delivery failures themselves, and most local extension models, such as IDE plugins or Obsidian’s ecosystem, ask the user to trust every author whose extension they install, which is a workable tradeoff for a notes app but not for a system holding financial transactions or private messages. Salesforce solved a version of this problem two decades ago by building its own compiler, runtime, and standard library so that customer logic could run safely inside a shared multi-tenant platform, well before serverless made that pattern common.

What makes the current moment diffferent is that the primitives Salesforce had to build from scratch now exist off the shelf, whether as V8 isolates, WebAssembly with WASI, or microVMs, and each comes with a different tradeoff between cold start latency, isolation strength, and language flexibility. Morrell’s argument for an object capability model over a traditional API proxy is worth sitting with regardless of which runtime a team chooses, since restricting untrusted code to explicit, narrow references such as getApprovedEmail() rather than a scoped API token removes an entire category of exfiltration risk by construction, instead of relying on a proxy layer to catch every malicious pattern after the fact.

For teams building internal tooling or customer facing platforms, the practical takeaway is that extensibility is no longer purely a UX or roadmap decision, it is also an infrastructure decision, and the primitive chosen early on will determine how much custom logic a platform can safely absorb once users start asking an LLM to write it for them.

https://jeremymorrell.dev/blog/extensible-software-in-the-age-of-llms/

Watermark

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It took only days to have an easy way to remove the watermarks. There are already ~70 repos:

https://github.com/search?q=watermark+remover+created%3A%3E2026-08-04&type=repositories

Posted in AI

World Bank: The Promise of Artificial Intelligence

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Elon Musk knows how to promote himself, he said recently that AI and robots will replace all jobs, and that working will become optional. It is a memorable line, and it travels fast. I read last week the World Bank’s World Development Report 2026: The Promise of Artificial Intelligence, and the picture it paints is far more grounded. It is the first comprehensive assessment of what AI means for the 6.8 billion people living in low and middle income countries, and the jobs finding deserves attention. Roughly 4.5 percent of jobs in those economies are amenable to automation by generative AI, against 14.2 percent in high income countries, while 16.2 percent are amenable to being complemented by AI. The report is direct about it: AI is not yet replacing large numbers of jobs in most developing countries. For most of the world, the immediate story is augmentation, not elimination.

I am an optimistic guy but not to the level of Pollyanna. The report is candid about real pressure on call centres and business process outsourcing, about early evidence that entry level white collar postings have thinned in India and China (i see this in my banking industry for sure), and about the dependency risk that comes with a handful of companies controlling the frontier. But the binding constraint it identifies is not runaway machines. It is reliable electricity, internet access, education, and local language data. In Sub-Saharan Africa, nearly one third of rural schools still lack dependable power. It argues that countries should be blinded neither by the hype nor the hysteria, and offer a plain framework instead: adopt, adapt, then advance. What stays with me is how much of this looks less like a technology story and more like an old fashioned development story wearing new clothes.

https://www.worldbank.org/en/publication/wdr2026

The collapse of the web as we know it

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On March 5th, TheNumbers.com, the film industry’s most trusted box office database, went dark without warning. It came back a week later stripped down, missing historical charts, movie pages, and its report builder. The cause was not a single dramatic event, it was a slow collapse under a combination of pressures that most small, independent websites are not built to survive. Founder Bruce Nash later revealed that only 10% of the site’s traffic came from actual human visitors, the rest was AI crawlers and agentic bots hitting a thirty year old system with 160,000 legacy files. Buried in that traffic were also signs of deliberate probing, likely aimed at accessing box office data before it went public, since prediction markets use those numbers to settle real money bets.

The deeper issue here is not that one site got hacked, it is that the assumptions the open web was built on no longer hold. Data quality and traffic volume are no longer reliable indicators of a site’s health, resilience against automated extraction now matters just as much, and any business relying on a single, aging web presence should treat that infrastructure as a liability to be actively managed, not a static asset to be left alone. And scarier, for the small website mantainer, it is a Davi-Goliath battle that most of them does not know how to fight.

https://stephenfollows.com/p/what-just-happened-to-thenumberscom-should-worry-us-all

Entire.io

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I just got my invire for entire.io, it is really cool, incredibly ui, easy cli. It does not track only what changed in the commit, it captures the entire session I had with claude, the prompt, answers, tool calls, token usage, top! I was mantaining a very naive tracker keeping the guids so I could return to a session, dont need it anymore. well, until they charge me an arm and a leg 🙂

https://github.com/sergiorgiraldo/entire101/