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When I started building , I needed reliable access to Instagram data. Like many developers, my first instinct was to use a self-hosted solution such as instagrapi. It worked for experimenting, but once I started depending on it for production workflows, I spent more time maintaining the scraper than building features. Eventually I switched to HikerAPI, a hosted REST API for Instagram. This post isn't about saying one approach is universally better—it's about why it ended up being the better fit for my project. My use case I needed to fetch Instagram profile data for . The requirements were fairly simple: Look up public profiles Process structured JSON Integrate the results into my backend Avoid spending time maintaining login sessions I wasn't interested in reverse engineering Instagram every time something changed. Getting started One thing I liked was that it behaves like a normal REST API. Authentication is done through an x-access-key header, so integrating it into an existing Python backend took only a few minutes. import requests headers = {"x-access-key": "YOUR_KEY"} r = requests.get( " https://api.hikerapi.com/v2/user/by/username?username=instagram ", headers=headers, ) print(r.json()) That's enough to start requesting data and integrating it into your own application. If you want to explore the API, you can find it at HikerAPI. Why I moved away from self-hosted scraping I originally tried , including instagrapi. There wasn't a single issue that made me switch—it was the accumulation of small operational problems: Login sessions expiring Accounts getting challenged Temporary bans Instagram changing internal behavior Regular maintenance after updates None of those problems are impossible to solve. The question became whether solving them was the best use of my time. For my project, the answer was no. I'd rather focus on shipping features than maintaining scraping infrastructure. Tradeoffs Using a hosted API isn't free. Pricing starts at $0.001 per request, wi

I think a lot of people still imagine AI coding as opening ChatGPT, asking for code, and copy-pasting the result. That's not really how I work anymore. The biggest shift for me is that planning matters far more than coding. Earlier, execution was expensive, so most of the effort went into writing code. Now execution is cheap. I can have an agent implement something in minutes. The hard part is making sure the plan is correct. Most of my effort goes into thinking through the architecture, edge cases, failure modes, test strategy, and how the change fits into the broader system. If the plan is vague, the agent will confidently implement the wrong thing. The quality of the result is mostly determined by the quality of the plan. Once I have a plan, I break it into small independent pieces. Each piece should be executable without additional clarification. If an agent needs to stop and ask questions, the task probably isn't broken down enough. Those pieces become tickets. Then an agent picks up a ticket and implements it. The important thing is that the agent isn't operating in a vacuum. I try to give it a good environment to work in: Clear architectural rules Reusable skills and workflows Guardrails Hooks for things that must always happen One lesson that really stuck with me is that instructions are guidance, not guarantees. At one point I had "always use a git worktree" written in AGENTS.md. The model still ignored it occasionally. When I dug into it, the answer was simple: models can drift from instructions. So if something absolutely must happen, don't rely on instructions. Enforce it. Put it in a hook, script, validation step, CI check, or some other deterministic mechanism. If it is important, make it impossible to skip. Once the implementation is done, the agent opens a PR. This is where another useful pattern comes in: don't let the same model review the code it wrote. I usually have one model implement and another model review. Different models catch different t

African payments infrastructure company Flutterwave has hit a new valuation and landed blockchain company Ripple as investor and partner.

Data privacy regulations such as GDPR, LGPD, and HIPAA demand that organizations know exactly where Personally Identifiable Information (PII) lives inside their databases. Yet in practice, most teams rely on manual inventories, tribal knowledge, or external scanning tools that require data to leave the database engine — a process that itself creates privacy and security risks. This article presents an MVP that takes a different approach: it runs PII detection inside InterSystems IRIS using Embedded Python, analyzing data where it lives and never exporting it to an external process. The result is a lightweight, non-intrusive utility that scans your tables, identifies PII using AI, and produces a structured CSV report — all without data ever leaving the IRIS process. The Problem: PII You Don't Know You Have Organizations today face a painful blind spot. A typical IRIS instance may contain hundreds of tables across dozens of schemas, some holding decades of accumulated data. Columns named ContactInfo , Notes , or Description might silently contain social security numbers, email addresses, or government IDs — sometimes intentionally, sometimes as a side effect of free-text fields that capture whatever users type in. Traditional approaches to PII discovery share a common flaw: they require data extraction. You export samples, send them to an external service, or pipe them through a standalone tool. Every step in that pipeline is an additional attack surface and a potential compliance violation. The principle of data sovereignty — keeping data within its jurisdiction and under controlled access — suggests a better path: bring the analysis to the data, not the data to the analysis. This is not just a technical preference; it is a governance requirement: GDPR (EU) — Article 28 requires that any processing of personal data by a third-party processor be governed by a binding contract covering subject-matter, duration, purpose, data types, and obligations [ Art. 28 GDPR ]. Art

Yes, first unplug it and plug it back in. But after that, here's what you can try to fix your slow Fire TV Stick.

After a brief debut at the Cannes film festival last month, DJI announced all the details of its Osmo Pocket 4P stabilized camera today as part of its initial launch in China. While it doesn't match the 8K capabilities of the Insta360 Luna Ultra, the Pocket 4P features a new 1-inch sensor with improved dynamic […]

Designers are finding sustainable building solves close to home—in ancient practices and cutting-edge innovations alike.

An AI agent has two kinds of memory: conversation (semantic) and context (exact reference). Keep them separate with Strands and AgentCore.

Verizon introduced a new plan that costs $45 per month, revamped rewards programs, and more today.

The Justice department says the Pentagon needs xAI to keep using its unpermitted gas turbines.

Tech has been encroaching on the family domicile for years—but actor, writer, and satirist Jill Kargman is all in on analog.

Against all odds, the Tesla Cybercab is in production. And while Elon Musk's company may not have a very coherent plan for the tiny, autonomous two-seater, it's still taking the necessary steps to certify the EV's legitimacy. As such, Tesla recently filed paperwork with the Environmental Protection Agency that reveal many of the Cybercab's specs, […]

Plaud is trying to make a mark in a crowded market full of AI-powered meeting notetakers.

By this time next year, Fox Corporation CEO Lachlan Murdoch intends to have added Roku to his already expansive media empire. Should the acquisition go through, Fox will gain control of Roku's modest library of original programming, and the newly combined company will become "the third-largest player in U.S. television" in terms of viewing share. […]

From friend compounds and meditation spaces to shaded outdoor areas and rooms just to make coffee, homes are getting even more multipurpose.

Unlike many of his tech industry peers who have cut thousands of jobs citing the need to restructure to make the most of AI, Robinhood's CEO Vlad Tenev conspicuously made no mention of AI in his note about layoffs.

Buying a first house used to mark entry into adulthood—and the beginning of wealth-building. But a shifting economic landscape is threatening to close the door on this American milestone.

The service will leverage its Moovit platform to launch in an a US city in 2027.

Transform songs into retro Gameboy-style game console music Discussion | Link

Insta360's first gimbal camera is a high-powered Osmo Pocket rival that you can actually buy.