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1046 posts · Page 7 of 88. ← Blog
Even an agent that thinks in code needs tools — the functions its code calls to reach beyond itself. smolagents defines them the same minimal way it does everything: a decorated Python function. The twist is that in a code agent, tools are called from within code, which is what makes composing them so natural.
Even an agent that thinks in code needs tools — the functions its code calls. smolagents defines them the same minimal way (a decorated function), and the twist is that tools are called from within code, which is what makes composing them so natural.
The model drives a Strands agent, so which model you use is the single biggest determinant of how well it works — and Strands keeps that a swappable choice across providers rather than locking you to one. Model-agnosticism isn't a convenience here; in a model-driven framework it's foundational.
The model drives a Strands agent, so which model you use is the biggest determinant of how well it works — and Strands keeps that a swappable choice across providers. Model-agnosticism isn't a convenience here; it's foundational.
The last inference lever is the classic cloud play in AI clothing: commit to capacity for the steady, predictable part of your load and pay less per unit. And underneath all the AI-specific tactics sits the ordinary cloud bill, where most of a mature estate's money actually lives — governed by the same rate, usage, and waste principles for decades.
The last inference lever is the classic cloud play in AI clothing: commit to capacity for the steady part of your load and pay less per unit. And underneath all the AI-specific tactics sits the ordinary cloud bill, where most of a mature estate's money lives.
Chains follow a path you define; agents decide the path themselves. LangChain gives you both the tools an agent uses and — increasingly through LangGraph — the machinery to run agent loops reliably. Understanding where LangChain's tools end and LangGraph's orchestration begins is the key to building agents that work rather than agents that wander.
Chains follow a path you define; agents decide the path themselves. LangChain gives you the tools an agent uses and — increasingly through LangGraph — the machinery to run agent loops reliably. Knowing where tools end and orchestration begins is the key.
A single agent run answers one question; a conversation needs memory, and a good user experience needs the answer to appear as it's generated. Pydantic AI handles both through its message system — the record of what was said that you pass between runs — and streaming, which delivers typed output progressively. Together they turn one-shot agents into conversational, responsive ones.
A single run answers one question; a conversation needs memory, and good UX needs the answer to appear as it's generated. Pydantic AI handles both through its message system — the record you pass between runs — and streaming that delivers typed output progressively.
Every compliance control eventually produces something you must tell a regulator: a suspicious activity report, a transaction report, a periodic filing. Regulatory reporting is where your internal compliance data becomes an external, deadline-bound, precisely-formatted obligation — and it's an unglamorous data-engineering problem where accuracy and timeliness are legal requirements, not quality goals.
Every compliance control eventually produces something you must tell a regulator: a suspicious activity report, a transaction report, a periodic filing. Reporting is where internal compliance data becomes an external, deadline-bound obligation where accuracy and timeliness are legal requirements.
Almost nothing on the modern internet talks directly to the server that answers it. In between sit proxies and load balancers — the traffic directors that spread load across many servers, terminate TLS, cache responses, and shield your backends. Understanding this layer is understanding how a single domain name serves millions of users from hundreds of machines.
Almost nothing on the modern internet talks directly to the server that answers it. In between sit proxies and load balancers — the traffic directors that spread load, terminate TLS, cache, and shield your backends — turning one domain into a system that scales.
An alert that fires when nothing is actually wrong trains people to ignore alerts — and an ignored alert is worse than no alert, because it creates false confidence while the real incident scrolls past unnoticed. Good alerting is ruthlessly selective: page a human only for things that are both urgent and real, alert on what users feel, and treat every noisy alert as a bug to fix.
An alert that fires when nothing is wrong trains people to ignore alerts — and an ignored alert is worse than none. Good alerting is ruthlessly selective: page a human only for things both urgent and real, alert on what users feel, and treat every noisy alert as a bug.
Logging a user in is the easy part; keeping them logged in — and, harder than anyone expects, logging them out — is where identity gets subtle. Sessions bridge stateless requests into a continuous identity, single sign-on shares that identity across apps, and single logout is a genuinely hard problem that most systems get partly wrong.
Logging a user in is the easy part; keeping them logged in — and, harder than anyone expects, logging them out — is where identity gets subtle. Single logout is a genuinely hard problem that most systems get partly wrong.
A fine-tune that looks great on a handful of hand-picked prompts can be quietly broken — overfit to your training data, worse than the base model you started from, or catastrophically forgetful of skills it used to have. Without real evaluation you can't tell, and shipping a fine-tune you haven't measured is shipping a guess.
A fine-tune that looks great on a handful of prompts can be quietly broken — overfit, worse than the base model, or catastrophically forgetful. Without real evaluation you can't tell, and shipping a fine-tune you haven't measured is shipping a guess.
Real search is never pure vector similarity. Users want "similar documents from this project, updated this year" and they expect an exact product code to match exactly. Combining similarity with metadata filters and keyword search — without wrecking recall — is where academic ANN meets production requirements, and it's harder than it looks.
Real search is never pure vector similarity. Users want 'similar documents from this project, updated this year' and expect an exact product code to match exactly — combining similarity with metadata filters and keyword search without wrecking recall is where ANN meets production.
On-device AI's biggest promise is privacy — but that promise is only real if the architecture actually keeps data on the device. Privacy isn't a feature you add; it's a property of a design where sensitive data has no path off the phone. This post is about building that property in deliberately, and about the honest hybrid designs for when pure local isn't enough.
On-device AI's biggest promise is privacy — but only if the architecture actually keeps data on the device. Privacy isn't a feature you add; it's a property of a design where sensitive data has no path off the phone.