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Previously, I wrote about How I Processed 666K Pages Of Flattened PDFs into a Full Text Search Engine called the Apario writer . Upon on the conclusion of the last segment, I was able to optimize the compilation time of the original collection of data by rewriting the sidekiq Ruby pipeline script into a dedicated Go Application. Regardless of what compiling the PDF assets would look like, I still needed to serve those assets - and that's where the writer did little to nothing to actually address the OPEX of the project from 2020. Given the size of the data set, the 666K pages ended up compiling into a directory of ~1.13TB in size. This was held in storage that was distributed across several high volume storage dedicated servers on OVH behind MinIO . This provided an S3 compatible API directly. What I Know About OPEX OPEX or Op erational Ex pense is how you describe a spending of money that is used explicitly for the operations of the business versus a capital expense. Hardware was considered a CAPEX or Cap ital Ex pense. So when Bit Fry Game Studios needed their DevOps pipeline upgraded for the 9 hour game builds into a 30 minute private enterprise cloud build, it required a CAPEX investment of $69K plus trust in me in order to achieve a -$15K/month OPEX savings. Annualized over a hardware lifecycle, over $472K can be recovered from OPEX by making a small CAPEX expense up front. One of the first projects that I ever worked on was in PHP and MySQL on Ubuntu 8.04 . It was to balance the budget of a department that had ACME Bucks so to speak. It required me to write a finance module, fully tested, that managed Blue , Green and Black dollars. Blue dollars were for OPEX. Green dollars were for CAPEX. Black dollars were for external vendors where money left the company (versus moving between departments). Black depreciated instantly - meaning 100% of it was paid immediately. Blue dollars were borrowed over a 12 month pay-back period. Green dollars were borrowed over a 36

On the latest episode of Equity, we discussed why Moonshot AI's Kimi seemed to panic Silicon Valley and Wall Street.

By Suvankar Chakraborty | Principal Engineer — IAM, Modern Workplace Management & IT Operations The Collaboration Platform That Became a Governance Nightmare Microsoft Teams was deployed at extraordinary speed across the enterprise world. In most organisations I know, the deployment timeline went something like this: March 2020, global pandemic, remote work mandate, Teams switched on, everyone told to use it, governance deferred because there was no time. Five years later, the governance that was deferred in 2020 has still not been implemented in most of those environments. The result is predictable and consistent across industries: Teams sprawl at industrial scale. Hundreds of Teams that nobody owns. Channels for projects that ended three years ago. Guest users from partnerships that dissolved. Sensitive conversations in channels that include contractors who should not have visibility. Files shared in Teams chat — bypassing SharePoint governance entirely — on devices with no management policy. Meeting recordings stored in OneDrive folders that anyone with a link can access. Bot integrations that have permissions to read your Teams messages and access your calendar, approved by a user who clicked through an OAuth consent screen without reading it. In 13+ years of enterprise IAM and IT operations work, Teams governance has become one of the most consistently mismanaged areas of Microsoft 365. Not because it is technically difficult — the controls Microsoft provides are comprehensive. But because Teams sits at the intersection of IT, security, compliance, and the organisational culture of collaboration, and that intersection is where governance programmes go to die. This article is about why enterprises get Teams governance wrong, and what getting it right actually looks like — in specific, actionable, implementable terms. Why Teams Is a Governance Problem Unlike Any Other M365 Workload To understand the governance challenge, you need to understand what Microsoft Team

Nota: ✋ This post was originally published on my blog wiki-cloud.co Introduction Artificial intelligence is evolving at an unprecedented pace and is transforming how people and businesses interact with technology. Over the past few years, much of the focus has been on generative AI models, which can create text, images, code, audio, and other types of content from natural language instructions. These capabilities have marked a significant and transformative shift in how we perform many tasks, allowing AI to move from a specialized technology to an accessible tool for millions of users. However, we are entering a new stage. Artificial intelligence models are no longer limited to simply answering questions or generating content. They can now be autonomous, understand objectives, analyze context, decide what steps to take, use tools, consult different sources of information, connect with APIs, execute actions, and collaborate with other specialized agents to complete more complex tasks. This evolution is giving rise to what is known as agentic artificial intelligence, an approach in which AI systems can act with a greater level of autonomy and actively participate in business, technical, and operational processes. Instead of simply offering a recommendation, an agent can search for information, validate data, coordinate different activities, and execute a sequence of actions aimed at achieving a specific goal. Within this new scenario appears Google Agent Development Kit , also known as Google ADK , is an open-source framework developed and designed by Google to facilitate the creation, evaluation, and deployment of artificial intelligence agents. ADK provides developers with a structure for defining agent behavior, connecting them to language models and external tools, managing sessions and memory, coordinating multi-agent systems, and evaluating their performance before deploying them to production. Thanks to this code-based approach, Google ADK allows you to build e

If you want to tweak your Android Auto settings, the software has a developer menu you can check out.

Part 1 documented the recurring snapback in practice. This note asks a narrower question: what does the observed pattern support, what remains a working hypothesis, and what changes should follow in the project? Status: Bounded project conclusion. This note separates observed behaviour, working hypothesis, and practical consequence. It is based on current project documents and interactions; it is not external validation or a universal claim about AI systems. What the evidence supports 1. The project already contains a stable relational model The working model is not generic “AI assistance.” It separates reasoning surfaces, uses bounded comparisons, permits two-way cognitive pressure, and keeps final acceptance authority with the human. Reciprocal cognitive contribution, asymmetrical governing authority. 2. Concrete project work preserves the structure better than public abstraction At the concrete level, instructions such as: Review this proposal against that architecture. preserve the distinction between the object being reviewed, the surface applying pressure, the evidence, and the authority that may accept a change. When the same structure was compressed into general prose, generated explanations repeatedly returned to a simpler one-way model of either human control or transferred AI authority. That is an observed pattern in this development process. 3. Public explanation is a separate reasoning surface A README, article, summary, or portfolio page is a projection of the model, not the model itself. It cannot be assumed to reproduce the internal structure faithfully merely because that structure is present in context. The explanation must be reviewed against the model it represents: Does this explanation preserve the actual authority, review, evidence, and state-transition structure? 4. Annoyance was useful boundary data The irritation indicated that the generic rendering was no longer merely an imperfect exploration. It was colliding with an internal frame that

Hey Techie 🌸 Before I continue my go series, I wanted to share a personal project that I'll be working on alongside my learning. What is IRIS? IRIS is an adaptive accessibility companion meant to help people with invisible disabilities navigate the media in ways preferable to them. Most websites and systems are one-size-fits-all and do not take user preferences into account in depth. The assumption is that every user views technology the same way, and that's not true at all. This is where IRIS shines her glory. The goal of creating IRIS is that it adapts to the user's needs rather than the user adapting to it. She will be able to personalise things like text-to-speech, colour themes, layouts, and other accessibility features based on their needs. As I continue learning Go and backend development, I'll also be sharing the progress of building IRIS, from designing the database and API to developing the backend and, eventually, the complete application. I look forward to sharing my progress and the challenges I will face and having discussions with you, my dear techie friends 🌸

I went looking for a simple answer to a simple question: How do you give an agent access to Google Calendar? Not a demo. Not a screenshot. A real agent, running unattended, with enough access to be useful and enough guardrails that it won’t turn into a security incident. While researching OpenClaw setups, I found a thread on r/openclaw where someone asked what looked like a tiny question: what do I need to add Google Calendar to OpenClaw? One reply said: "Look into gog cli." That answer is way more revealing than it looks. Because the hard part usually isn’t Google Calendar itself. The hard part is everything hidden behind the phrase "connect Google" . And if you’re building agents in n8n, Make, Zapier, OpenClaw, or a custom OpenAI-compatible loop, auth is only half the problem anyway. Once the workflow runs 24/7, you also need to think about retries, quota limits, caching, and how many LLM calls the thing is quietly making in the background. That’s where a lot of teams hit the same wall: the integration works, but the operational shape of it is bad. Security is fuzzy. Request volume is noisy. And AI costs get weird fast if every poll and retry triggers more model calls. The demo version is lying to you If you’ve used something like n8n Cloud, you’ve seen the polished version: Click Google Calendar Sign in Approve access Done That flow is real inside a managed product. But the minute you leave the managed garden — self-hosted n8n, OpenClaw, a custom MCP server, a Python worker on Ubuntu, or your own app using the OpenAI SDK against an OpenAI-compatible endpoint — you inherit the boring parts. Now "connect Google" actually means: create a Google Cloud project configure the OAuth consent screen choose the right OAuth client type enable the Google Calendar API pick the right scopes store credentials safely handle refresh tokens deal with quota errors later That’s not setup trivia. That’s infrastructure. One user in that same OpenClaw discussion realized it immediately:

If your automation just stopped mid-run, you have hit a quota limit. Here is exactly which one and how to fix it — free, no upgrade required, works at any order volume. Quick answer — the numbers that matter in 2026: Both consumer (free) and Google Workspace accounts get a 6-minute maximum execution time per script run — the old 30-minute Workspace limit no longer applies. Consumer accounts are capped at 90 minutes of trigger runtime per day; Workspace accounts get 6 hours of trigger runtime per day. UrlFetch calls are capped at 20,000 per day on consumer accounts (100,000 on Workspace). These are the hard limits that cannot be increased — they are why Autocrat, Sheets automations, and document workflows fail at scale. If you have ever seen “Service invoked too many times”, “Exceeded maximum execution time”, or “Could not obtain lock”, you already know the symptom. This guide explains the exact numbers behind those errors, when they appear by account type, and what actually works when order or document volume gets serious. What Are Google Apps Script Quotas? Google Apps Script quotas are hard limits on how much work a script can do. They exist to protect shared infrastructure — stopping one workflow from consuming resources that affect thousands of other users. Quotas appear across four layers, and they all apply simultaneously: User-level quotas — tied to the Google account running the script. Consumer and Workspace accounts have different ceilings. Project-level quotas — tied to the Apps Script project itself. Concurrent executions are capped here. Service quotas — Gmail, Sheets, Drive, UrlFetch, and other services each have their own daily limit. Execution quotas — limits on how long a single run can take and how much total runtime is consumed in a day. The critical point is that these limits stack independently. A workflow can be fine on execution time but fail on service call rate — which is why the same script can work perfectly for a small operation and break

The lawsuit will likely renew debates about whether term employment agreements are enforceable under California. law

Also: Forging the One Ring in Rings of Power S3 teaser; new Lanterns trailer; Spaceballs: The New One panel.

Google's Vice President of Devices and Services, Shakil Barkat, all but confirmed in an interview with 9to5 Google that its next Pixel phone would cost more than the Pixel 10. Considering the ongoing RAM supply issues due to the explosion of AI data centers, the rumored price hike is not a complete surprise. Companies from […]

GoPro’s New Mission 1 cameras bring action cameras to a higher level with a larger sensor and more cinematic footage.

Most agent platforms give you one configuration file and hope. When you are running agents for more than one team — or more than one customer — a single config breaks down fast. Each workspace needs its own model, its own service access, its own secrets, but someone has to guarantee that no workspace can spend more than its budget or reach a service it was never authorized to use. The answer is a policy cascade. Every setting — model, temperature, allowed services, API keys, skill availability, TTL defaults — resolves through three ordered tiers: company, repo, and workspace. A lower tier can narrow an upstream ceiling but never widen it. That single rule is what makes it safe to hand a workspace to a team without handing them the keys. How the cascade resolves Three tiers, bottom-up. The company tier sets the floor. The repo tier overrides it. The workspace tier overrides both. The resolution order is fixed for every kind of setting: Kind Company tier Repo tier Workspace tier Policy fields defaults override override Variables floor override override Skills floor override override Secrets last-resort fallback override first-resolved Notice that secrets run in reverse. A workspace-tier credential wins over the repo and company defaults, and an empty value at the workspace tier falls through to the repo. This means you set a per-customer token where it is used and fall back up the chain only when it is absent. You are never forced to duplicate credentials across every workspace. Narrow-only: the safety property The cascade is not a free-for-all override. For service access, budgets, quotas, and TTL defaults, a lower tier can only narrow what the tier above it allows. If the company grants a repo [github, slack, search] , a workspace under that repo can select a subset — [github, search] — but it cannot add linkedin . The resolve-time intersection is enforced, not advisory. This extends to per-service API surface control. Granting access to GitHub does not mean grantin

The European Commission found Google's search ranking and anti-steering violated the Digital Markets Act.

Australia was the first country to issue a ban in late 2025, aiming to reduce the pressures and risks that young users may face on social media, including cyberbullying, social media addiction, and exposure to predators.

Lower catch this year . As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered. Blog moderation policy.

A U.S. citizen has asked a court to throw out the government's claim that he gave over a passcode to border authorities that wiped his phone's data, opening up fresh questions about a person's constitutional rights at the U.S. border.

The web and Google once had a deal: Google collects data and indexes webpages and in exchange sends oceans of traffic to websites. The deal wasn't perfect and certainly made Google more money than it made the websites, but it worked for a long time. Now, however, the deal seems to be dead. And the […]

The offline messaging app surged in popularity in India amid protests in New Delhi.