TOPIC: CROSS-PLATFORM SOFTWARE
Loading API Keys from Linux shell environment variables in Python with Dotenv
23rd October 2025Recently, I ran into trouble with getting Python to pick up an API key that I had defined in the underlying bash environment. This was within a Python console running inside the Positron IDE for R and Python scripting. Opening up the folder containing my Python scripts within the IDE was part of the solution. The next part was creating a .env file within the same folder. A line like this was added within the new file:
export API_KEY="<API key value>"
That meant that code like the following then read in the API key in a more robust manner:
import os
from dotenv import load_dotenv
load_dotenv()
api_key = os.getenv('API_KEY', 'default_value')
This imports the os module and the load_dotenv method from the dotenv package. Then, load_dotenv is executed to load the .env file and its contents. After that, the os.getenv function can assign the API key to a Python variable from the value of the environment variable.
Since this also was within a Git repository, a .gitignore file needed creating with the contents .env to avoid that file being uploaded to GitHub, which is the last place where you should be storing credentials like passwords, passphrases and API keys. While my repository may be private, the state of things at these troubled times mean that even that is no failsafe.
Taking control of Ruff checks on Python scripts
22nd October 2025Positron is becoming my tool of choice for developing Python code. Along from using a Python console like a REPL environment, it also includes Ruff for checking code compliance. One of its rules is that Python modules must be declared at the top. However, I want to use some code that checks for the present of any modules used in a script, installing those that are missing. This means that import statements appear later in a script that Ruff recommends, making me wish for a way to turn off that check since things run well anyway. The chosen solution is to create a file called pyproject.toml in the directory where my scripts are store and add the following lines in there to accomplish what I want:
[tool.ruff]
ignore = ["E402"]
Here, it helps if you open a folder in Positron, achieving the same outcome as you would in the VSCode on which the IDE is based. While I have only listed one check here, you also can have a comma-delimited list of quoted strings if you need to switch off more than one rule at once.
Controlling the version of Python used in the Positron console with virtual environments
21st October 2025Because I have Homebrew installed on my Linux system for getting Hugo and LanguageTool on there, I also have a later version of Python than is available from the Linux Mint repositories. Both 3.12 and 3.13 are on my machine as a consequence. Here is the line in my .bashrc file that makes that happen:
eval "$(/home/linuxbrew/.linuxbrew/bin/brew shellenv)"
The result is when I issue the command which python3, this is what I get:
/home/linuxbrew/.linuxbrew/bin/python3
However, Positron looks to /usr/bin/python3 by default. Since this can get confusing, setting a virtual environment has its uses as long as you create it with the intended Python version. This is how you can do it, even if I needed to use sudo mode for some reason:
python3 -m venv .venv
When working solely on the command line, activating it becomes a necessity, adding another manual step to a mind that had resisted all this until recently:
source .venv/bin/activate
Thankfully, just issuing the deactivate command will do the eponymous action. Even better, just opening a folder with a venv in Positron saves you from issuing the extra commands and grants you the desired Python version in the console that it opens. Having run into some clashes between package versions, I am beginning to appreciate having a dedicated environment for a set of Python scripts, especially when an IDE makes it easy to work with such an arrangement.
Avoiding Python missing package errors with automatic installation checks
20th October 2025Though some may not like having something preceding package import statements in Python scripts, I prefer the added robustness of an extra piece of code checking for package presence and installing anything that is missing in place getting an error. In what follows, I define the list of packages that need to be present for everything to work:
required_packages = ["pandas", "tqdm", "progressbar2", "sqlalchemy", "pymysql"]
Then, I declare the inbuilt modules in advance of looping through the list that was already defined (adding special handling for a case where there has been a name change):
import subprocess
import sys
for package in required_packages:
try:
__import__(package if package != "progressbar2" else "progressbar")
print(f"{package} is already installed.")
except ImportError:
print(f"{package} not found. Installing...")
subprocess.check_call([sys.executable, "-m", "pip", "install", package])The above code tries importing the package and catches the error to do the required installation. While a stable environment may be a better way around all of this, I find that this way of working adds valuable robustness to a script and automates what you would need to do anyway. Though the use of requirements files and even the Poetry tool for dependency management may be next steps, this approach suffices for my simpler needs, at least when it comes to personal projects.
Python productivity: Building better code through design, performance and scale
12th September 2025Python's success in data science and beyond stems from more than just readable syntax. It represents a coherent philosophy where errors guide development, explicitness prevents bugs, modern tooling enforces quality, performance comes from purpose-built engines, and scaling extends rather than replaces familiar patterns. Understanding these principles transforms everyday coding from a series of individual tasks into a systematic approach to building robust, maintainable and efficient systems.
- Error-Driven Development as a Design Philosophy
Python treats errors not as failures, but as design features that surface problems early and prevent subtle defects later. The language embodies an "easier to ask forgiveness than permission" philosophy, attempting operations first and objecting meaningfully when they cannot proceed.
Consider how Python handles basic operations. A SyntaxError appears immediately when code violates grammatical rules: if True print("hello") triggers an immediate complaint with a caret pointing to the problematic location. Python neither guesses intentions nor continues with broken syntax because this guarantee of clear structure keeps code understandable across projects and platforms.
Sequence operations demonstrate similar principles. When code attempts to access lst[5] on a three-element list, Python raises IndexError: list index out of range rather than silently padding or expanding the sequence. This deliberate failure prevents hidden logic errors in loops and aggregations by forcing explicit checks of assumptions about data size.
Dictionary lookups follow the same pattern. Accessing a non-existent key with d['missing'] yields KeyError: 'missing' rather than inventing placeholder values. This explicit failure catches typos and unclear control flow whilst enabling defensive programming patterns through try/except blocks.
Name resolution errors like NameError and UnboundLocalError enforce clear scoping rules without creating variables accidentally or resolving names to unexpected contexts. Type discipline appears at runtime through TypeError for incorrect argument types and ValueError for correct types with inappropriate values. Each error message identifies which contract has been violated, directing fixes to either the object passed or the value it contains.
Assertions provide a final layer of optional verification. The assert statement allows code to state assumptions explicitly, failing with meaningful messages when invariants do not hold. This narrows the search space for defects by making expectations visible and providing immediate context for failures.
Taking these error signals seriously nudges development towards explicitness and clarity, establishing a foundation for all subsequent quality improvements.
- Explicitness Over Implicitness
Making intentions clear through code structure prevents ambiguity, aids tooling and simplifies reuse. This principle manifests across multiple areas of Python development, from data structures to function signatures.
Raw dictionaries offer flexibility but create fragility. A typo in a key or missing field becomes a runtime KeyError with no contract about required contents. Using @dataclass to define structured objects like User with id, email, full_name, status and optional last_login provides clear interfaces with minimal overhead. Type hints and IDE support make attribute access unambiguous, whilst construction fails early when required fields are absent.
For cases requiring validation, pydantic models build on this foundation. An email field declared as EmailStr automatically validates format, while custom validators can restrict status values to specific options such as 'active', 'inactive' or 'pending'. The resulting models are self-documenting and shield downstream code from invalid data.
Function parameters representing closed sets of options benefit from similar treatment. Plain strings invite typos and lack autocomplete support. Defining enums such as OrderStatus with PENDING, SHIPPED and DELIVERED makes possible states explicit whilst helping both developers and tools. Passing OrderStatus.SHIPPED to process_order reveals intention clearly and enables straightforward comparisons against enum members.
Function signatures become clearer through keyword-only arguments, enforced with a bare star in definitions. A function like create_user(name, email, *, admin=False, notify=True, temporary=False) forces call sites to write create_user(..., admin=True, notify=False) rather than passing sequences of ambiguous boolean values. The resulting calls read almost as documentation.
File path operations improve through object-oriented design. The pathlib module treats paths as objects where joining uses natural / syntax, directory creation uses mkdir, suffix changes use with_suffix, and text operations use read_text and write_text. Code becomes shorter, more portable and less prone to string manipulation errors.
These patterns consistently replace implicit assumptions with explicit contracts, making code intention more visible and reducing the cognitive load of understanding system behaviour.
- Structural Code Quality Through Tooling and Patterns
Sustainable code quality emerges from systematic approaches to organisation, testing and maintenance rather than individual discipline alone. Several key patterns and tools work together to create robust, readable codebases.
Control flow benefits from handling error conditions early rather than nesting deeply. Guard clauses invert the traditional structure so that invalid states return immediately, whilst main logic remains non-indented when preconditions are met. A process_payment function checking order.is_valid, then user.has_payment_method, then available funds before performing charges reads linearly. Exceptions during processing are caught precisely, errors logged with context, and functions return deterministically.
Even beloved list comprehensions have limits. When filtering and transformation logic become complex, sprawling comprehensions become opaque. Extracting predicates into named functions like is_valid_premium_user restores readability by giving conditions clear names. Where multiple checks and transformations are needed, conventional loops with early continue statements may prove more straightforward and debuggable.
Pure functions that accept all inputs as parameters and return results without changing external state simplify testing and reuse. Moving from designs where functions mutate global totals and read from global inventories to approaches where calculations accept prices, quantities and discounts as inputs removes hidden coupling. This enables deterministic testing of edge cases and reasoning about code without tracking changing state.
Documentation ties these practices together. Docstrings explaining what functions do, parameters they accept, values they return and including examples make codebases self-explanatory. Combined with tooling, docstrings serve as both reference and executable documentation.
Automation enforces consistency where human attention falters. Formatters like Black, linters like Ruff, static type checkers like mypy and import organisers like isort can run before each commit using pre-commit. Style issues and common mistakes are caught automatically, freeing mental capacity for higher-level concerns.
When handling errors, resist blanket except: statements that swallow everything from syntax errors to keyboard interrupts. Be specific where possible, catching ConnectionError, ValueError or database errors and handling each appropriately. When catch-alls are necessary, prefer except Exception as e: and log full tracebacks so that unexpected failures remain visible and traceable.
- Performance Through Modern Engines
Once code achieves cleanliness and robustness, performance becomes the next frontier. Traditional tools often leave substantial speed gains on the table, particularly for data-intensive work where single-threaded processing creates bottlenecks on modern hardware.
Polars, a DataFrame library written in Rust, addresses these limitations by making parallelism the default whilst providing both eager and lazy execution modes. Benchmarks on datasets of around 580,000 rows show Polars completing filtering roughly four times faster than Pandas, aggregation over twenty times faster, groupby operations eight times faster, sorting three times faster, and feature engineering five times faster. These gains stem from fundamental architectural differences rather than incremental optimisations.
The performance improvement requires a shift in mental model. Instead of writing sequential operations that execute immediately, you can batch expressions and let Polars parallelise them automatically. Creating both profit and margin with one with_columns call signals that these calculations can proceed together. Lazy evaluation extends this approach further. Building pipelines with pl.scan_csv('large_file.csv').filter(...).group_by(...).agg(...).collect() lets Polars construct query plans, then optimises them before execution. Filters are pushed down so less data reaches later stages, only selected columns are read, and compatible operations are combined.
Expressiveness comes from an expression system applying operations across columns succinctly. Where Pandas encourages thinking in terms of single columns assigned individually, Polars supports expressions like pl.col(['revenue', 'cost']) * 1.1 applied to multiple columns simultaneously. Familiar transformations translate directly: pl.read_csv('sales.csv') replaces pd.read_csv, selection and filtering become df.filter(pl.col('order_value') > 500).select(['customer_id', 'order_value']), new columns are created with df.with_columns(((pl.col('revenue') - pl.col('cost')) / pl.col('revenue')).alias('profit_margin')), and operations utilise all available cores automatically.
Memory efficiency improves through Apache Arrow's columnar format, storing data more compactly and avoiding NumPy-based overhead. CSV files of around 2 GB requiring roughly 10 GB of RAM in Pandas often process in approximately 4 GB with Polars. This difference can determine whether workflows run smoothly on laptops or require chunking strategies.
- Scaling Beyond Single Processes
When single processes reach their limits, two prominent approaches help scale Python across cores and machines whilst preserving familiar patterns and mental models.
Dask extends NumPy, Pandas and scikit-learn idioms to larger-than-memory datasets by partitioning arrays, DataFrames and computations then scheduling them in parallel. Its primary abstractions are dask.dataframe and dask.array, along with delayed task graphs. It excels for scalable batch processing, feature engineering and out-of-core work where the mental model remains close to the PyData stack. Integration with scikit-learn and XGBoost is mature, work-stealing schedulers are sophisticated, and detailed dashboards provide visibility. Clusters can be managed natively or through systems like Kubernetes and YARN.
For large-scale data cleaning and feature engineering, Dask provides natural extensions. Reading many CSV files from storage with dd.read_csv('s3://data/large-dataset-*.csv'), filtering rows with df[df['amount'] > 100], applying transformations per partition, then writing Parquet with df.to_parquet('s3://processed/output/') looks like Pandas but runs in parallel and out of core. Array computations through dask.array handle chunked operations so that x.mean(axis=0).compute() runs across partitions without exhausting memory.
Ray takes a more general approach to distributed computing through remote functions and actors. It suits workloads with many independent Python functions, stateful services and complex machine learning pipelines. A growing ecosystem includes Ray Tune for hyperparameter optimisation, Ray Train for multi-GPU training, Ray Serve for model serving, and RLlib for reinforcement learning. Scheduling is dynamic and actor-based, cluster management integrates with cloud providers, and scalability handles applications requiring control and flexibility.
For model training requiring many configuration explorations, Ray Tune provides schedulers and search strategies. Training functions can be wrapped and launched across workers with tune.run, with methods like ASHA stopping unpromising runs early. Integration with popular libraries means scaling experiments requires minimal code changes. Ray Serve turns model classes exposing __call__ methods into scalable services with @serve.deployment and serve.run, handling routing and scaling automatically.
- Incremental Adoption and Pragmatic Choices
The most sustainable approach to improving Python productivity involves gradual implementation rather than wholesale changes. Each improvement builds on previous ones, creating compound benefits over time whilst minimising disruption to existing workflows.
Adopting Polars illustrates this principle well. The first step can be simply loading data with pl.read_csv('big_file.csv') for faster I/O, then converting to Pandas with .to_pandas() if the rest of the pipeline expects Pandas objects. As comfort grows, expression-oriented patterns yield dividends: filtering then adding multiple columns in single chained calls, so Polars can optimise across steps. Full benefits appear when entire pipelines are expressed lazily, but this transition can happen gradually as understanding deepens.
Similarly, clean code practices can be introduced incrementally. Start by letting error messages guide fixes rather than suppressing them. Refactor one fragile dictionary into a dataclass when maintenance becomes painful. Extract a complex list comprehension into a named function when debugging becomes difficult. Each change teaches principles that apply more broadly whilst delivering immediate benefits.
Scaling decisions are often pragmatic rather than theoretical. If work centres on DataFrames and arrays with minimal conceptual shift from Pandas or NumPy, Dask likely delivers what you need. If workloads mix training, tuning and serving or require orchestrating many concurrent Python tasks with fine-grained control, Ray's abstractions and libraries provide better matches. Trying each approach on representative workflow slices quickly clarifies which will serve best.
The choice between tools should be driven by actual requirements rather than perceived sophistication. A single machine with Polars may outperform a small cluster running Pandas. A well-structured monolithic application may be more maintainable than a prematurely distributed system. The key is understanding when complexity serves genuine needs rather than adding overhead.
- Synthesis: The Compound Nature of Python Productivity
These themes work together rather than in isolation. Error-driven development creates habits that surface problems early. Explicit code structures make intentions clear to both humans and tools. Quality practices through tooling and patterns create sustainable foundations. Modern engines provide performance without sacrificing readability. Scaling approaches extend familiar patterns rather than replacing them. Incremental adoption ensures changes compound rather than disrupt.
The result is a coherent approach to Python development where each improvement reinforces others. Explicit data structures work better with static type checkers. Pure functions are easier to test and parallelise. Clean error handling integrates naturally with distributed systems. Modern DataFrame engines benefit from lazy evaluation patterns that also improve code clarity.
This synthesis explains Python's enduring appeal in data science and beyond. The language welcomes beginners with approachable syntax, whilst scaling to demanding production work without losing clarity. The ecosystem encourages practices that speed up teams over time rather than optimising for immediate gratification. The same principles that guide small scripts apply to large systems, creating a path for continuous improvement rather than periodic rewrites.
Start small: let error messages guide one fix, refactor one fragile dictionary into a dataclass, switch one slow operation to Polars, or run one hyperparameter sweep with Ray Tune. The improvements compound, and the foundations established early enable sophisticated capabilities later without fundamental changes to approach or mindset.
Fixing Python path issues after Homebrew updates on Linux Mint
30th August 2025With Python available by default, it is worth asking how the version on my main Linux workstation is made available courtesy of Homebrew. All that I suggest is that it either was needed by something else or I fancied having a newer version that was available through the Linux Mint repos. Regardless of the now vague reason for doing so, it meant that I had some work to do after running the following command to update and upgrade all my Homebrew packages:
brew update; brew upgrade
The first result was this message when I tried running a Python script afterwards:
-bash: /home/linuxbrew/.linuxbrew/bin/python3: No such file or directory
The solution was to issue the following command to re-link Python:
brew link --overwrite python@3.13
Since you may have a different version by the time that you read this, just change 3.13 above to whatever you have on your system. All was not quite sorted for me after that, though.
My next task was to make Pylance look in the right place for Python packages because they had been moved too. Initial inquiries were suggesting complex if robust solutions. Instead, I went for a simpler fix. The first step was to navigate to File > Preferences > Settings in the menus. Then, I sought out the Open Settings (JSON) icon in the top right of the interface and clicked on it to open a JSON containing VSCode settings. Once in there, I edited the file to end up with something like this:
"python.analysis.extraPaths": [
"/home/[account name]/.local/bin",
"/home/[account name]/.local/lib/python[python version]/site-packages"
]Clearly, your [account name] and [python version] need to be filled in above. That approach works for me so far, leaving the more complex alternative for later should I come to need that.
An Overview of MCP Servers in Visual Studio Code
29th August 2025Agent mode in Visual Studio Code now supports an expanding ecosystem of Model Context Protocol servers that equip the editor’s built-in assistant with practical tools. By installing these servers, an agent can connect to databases, invoke APIs and perform automated or specialised operations without leaving the development environment. The result is a more capable workspace where routine tasks are streamlined, and complex ones are broken into more manageable steps. The catalogue spans developer tooling, productivity services, data and analytics, business platforms, and cloud or infrastructure management. If something you rely on is not yet present, there is a route to suggest further additions. Guidance on using MCP tools in agent mode is available in the documentation, and the Command Palette, opened with Ctrl+Shift+P, remains the entry point for many workflows.
The servers in the developer tools category concentrate on everyday software tasks. GitHub integration brings repositories, issues and pull requests into reach through a secure API, so that code review and project coordination can continue without switching context. For teams who use design files as a source of truth, Figma support extracts UI content and can generate code from designs, with the note that using the latest desktop app version is required for full functionality. Browser automation is covered by Playwright from Microsoft, which drives tests and data collection using accessibility trees to interact with the page, a technique that often results in more resilient scripts. The attention to quality and reliability continues with Sentry, where an agent can retrieve and analyse application errors or performance issues directly from Sentry projects to speed up triage and resolution.
The breadth of developer capability extends to machine learning and code understanding. Hugging Face integration provides access to models, datasets and Spaces on the Hugging Face Hub, which is useful for prototyping, evaluation or integrating inference into tools. For source exploration beyond a single repository, DeepWiki by Kevin Kern offers querying and information extraction from GitHub repositories indexed on that service. Converting documents is handled by MarkItDown from Microsoft, which takes common files like PDF, Word, Excel, images or audio and outputs Markdown, unifying content for notes, documentation or review. Finding accurate technical guidance is eased by Microsoft Docs, a Microsoft-provided server that searches Microsoft Learn, Azure documentation and other official technical resources. Complementing this is Context7 from Upstash, which returns up-to-date, version-specific documentation and code examples from any library or framework, an approach that addresses the common problem of answers drifting out of date as software evolves.
Visual assets and code health have their own role. ImageSorcery by Sunrise Apps performs local image processing tasks, including object detection, OCR, editing and other transformations, a capability that supports anything from quick asset tweaks to automated checks in a content pipeline. Codacy completes the developer picture with comprehensive code quality and security analysis. It covers static application security testing, secrets detection, dependency scanning, infrastructure as code security and automated code review, which helps teams maintain standards while moving quickly.
Productivity services focus on planning, tracking and knowledge capture. Notion’s server allows viewing, searching, creating and updating pages and databases, meaning an agent can assemble notes or checklists as it progresses. Linear integration brings the ability to create, update and track issues in Linear’s project management platform, reflecting a growing preference for lightweight, developer-centred planning. Asana support provides task and project management together with comments, allowing multi-team coordination. Atlassian’s server connects to Jira and Confluence for issue tracking and documentation, which suits organisations that rely on established workflows for governance and audit trails. Monday.com adds another project management option, with management of boards, items, users, teams and workspace operations. These capabilities sit alongside automation from Zapier, which can create workflows and execute tasks across more than 30,000 connected apps to remove repetitive steps and bind systems together when native integrations are limited.
Two Model Context Protocol utilities add cognitive structure to how the agent works. Sequential Thinking helps break down complex tasks into manageable steps with transparent tracking, so progress is visible and revisable. Memory provides long-lived context across sessions, allowing an agent to store and retrieve relevant information rather than relying on a single interaction. Together, they address the practicalities of working on multi-stage tasks where recalling decisions, constraints or partial results is as important as executing the next action. Used with the productivity servers, these tools underpin a systematic approach to projects that span hours or days.
The data and analytics group is comprehensive, stretching from lightweight local analysis to cloud-scale services. DuckDB by Kentaro Tanaka enables querying and analysis of DuckDB databases both locally and in the cloud, which suits ad hoc exploration as well as embedded analytics in applications. Neon by neondatabase labs provides access to Postgres with the notable addition of natural language operations for managing and querying databases, which lowers the barrier to occasional administrative tasks. Prisma Postgres from Prisma brings schema management, query execution, migrations and data modelling to the agent, supporting teams who already use Prisma’s ORM in their applications. MongoDB integration supports database operations and management, with the ability to execute queries, manage collections, build aggregation pipelines and perform document operations, allowing front-end and back-end tasks to be coordinated through a single interface.
Observability and product insight are also represented. PostHog offers analytics access for creating annotations and retrieving product usage insights so that changes can be correlated with user behaviour. Microsoft Clarity provides analytics data including heatmaps, session recordings and other user behaviour insights that complement quantitative metrics and highlight usability issues. Web data collection has two strong options. Apify connects the agent with Apify’s Actor ecosystem to extract data from websites and automate broader workflows built on that platform. Firecrawl by Mendable focuses on extracting data from websites using web scraping, crawling and search with structured data extraction, a combination that suits building datasets or feeding search indexes. These tools bridge real-world usage and the development cycle, keeping decision-making grounded in how software is experienced.
The business services category addresses payments, customer engagement and web presence. Stripe integration allows the creation of customers, management of subscriptions and generation of payment links through Stripe APIs, which is often enough to pilot monetisation or administer accounts. PayPal provides the ability to create invoices, process payments and access transaction data, ensuring another widely used channel can be managed without bespoke scripts. Square rounds out payment options with facilities to process payments and manage customers across its API ecosystem. Intercom support brings access to customer conversations and support tickets for data analysis, allowing an agent to summarise themes, surface follow-ups or route issues to the right place. For building and running sites, Wix integration helps with creating and managing sites that include e-commerce, bookings and payment features, while Webflow enables creating and managing websites, collections and content through Webflow’s APIs. Together, these options cover a spectrum of online business needs, from storefronts to content-led marketing.
Cloud and infrastructure operations are often the backbone of modern projects, and the MCP catalogue reflects this. Convex provides access to backend databases and functions for real-time data operations, making it possible to work with stateful server logic directly from agent mode. Azure integration supports management of Azure resources, database queries and access to Azure services so that provisioning, configuration and diagnostics can be performed in context. Azure DevOps extends this to project and release processes with management of projects, work items, repositories, builds, releases and test plans, providing an end-to-end view for teams invested in Microsoft’s tooling. Terraform from HashiCorp introduces infrastructure as code management, including plan, apply and destroy operations, state management and resource inspection. This combination makes it feasible to review and adjust infrastructure, coordinate deployments and correlate changes with code or issue history without switching tools.
These servers are designed to be installed like other VS Code components, visible from the MCP section and accessible in agent mode once configured. Many entries provide a direct route to installation, so setup friction is limited. Some include specific requirements, such as Figma’s need for the latest desktop application, and all operate within the Model Context Protocol so that the agent can call tools predictably. The documentation explains usage patterns for each category, from parameterising database queries to invoking external APIs, and clarifies how capabilities appear inside agent conversations. This is useful for understanding the scope of what an agent can do, as well as for setting boundaries in shared environments.
In day-to-day use, the value comes from combining servers to match a workflow. A developer investigating a production incident might consult Sentry for errors, query Microsoft Docs for guidance, pull related issues from GitHub and draft changes to documentation with MarkItDown after analysing logs held in DuckDB. A product manager could retrieve usage insights from PostHog, review session recordings in Microsoft Clarity, create follow-up tasks in Linear and brief customer support by summarising Intercom conversations, all while keeping a running Memory of key decisions. A data practitioner might gather inputs from Firecrawl or Apify, store intermediates in MongoDB, perform local analysis in DuckDB and publish a report to Notion, building a repeatable chain with Zapier where steps can be automated. In infrastructure scenarios, Terraform changes can be planned and applied while Azure resources are inspected, with release coordination handled through Azure DevOps and updates documented in Confluence via the Atlassian server.
Security and quality concerns are woven through these flows. Codacy can evaluate code for vulnerabilities or antipatterns as changes are proposed, surfacing SAST findings, secrets detection problems or dependency risks before they progress. Stripe, PayPal and Square centralise payment operations to a few well-audited APIs rather than bespoke integrations, which reduces surface area and simplifies auditing. For content and data ingestion, ImageSorcery ensures that image transformations occur locally and MarkItDown produces traceable Markdown outputs from disparate file types, keeping artefacts consistent for reviews or archives. Sequential Thinking helps structure longer tasks, and Memory preserves context so that actions are explainable after the fact, which is helpful for compliance as well as everyday collaboration.
Discoverability and learning resources sit close to the tools themselves. The Visual Studio Code website’s navigation surfaces areas such as Docs, Updates, Blog, API, Extensions, MCP, FAQ and Dev Days, while the Download path remains clear for new installations. The MCP area groups servers by capability and links to documentation that explains how agent mode calls each tool. Outside the product, the project’s presence on GitHub provides a route to raise issues or follow changes. Community activity continues on channels including X, LinkedIn, Bluesky and Reddit, and there are broadcast updates through the VS Code Insiders Podcast, TikTok and YouTube. These outlets provide context for new server additions, changes to the protocol and examples of how teams are putting the pieces together, which can be as useful as the tools themselves when establishing good practices.
It is worth noting that the catalogue is curated but open to expansion. If there is an MCP server that you expect to see, there is a path to suggest it, so gaps can be addressed over time. This flows from the protocol’s design, which encourages clean interfaces to external systems, and from the way agent mode surfaces capabilities. The cumulative effect is that the assistant inside VS Code becomes a practical co-worker that can search documentation, change infrastructure, file issues, analyse data, process payments or summarise customer conversations, all using the same set of controls and the same context. The common protocol keeps these interactions predictable, so adding a new server feels familiar even when the underlying service is new.
As the ecosystem grows, the connection between development work and operations becomes tighter, and the assistant’s job is less about answering questions in isolation than orchestrating tools on the developer’s behalf. The MCP servers outlined here provide a foundation for that shift. They encapsulate the services that many teams already rely on and present them inside agent mode so that work can continue where the code lives. For those getting started, the documentation explains how to enable the tools, the Command Palette offers quick access, and the community channels provide a steady stream of examples and updates. The result is a VS Code experience that is better equipped for modern workflows, with MCP servers supplying the functionality that turns agent mode into a practical extension of everyday work.
Secure email services: Protecting your digital communications
21st August 2025In an era where digital privacy faces increasing threats from corporate surveillance, government oversight and cyberattacks, traditional email services often fall short of protecting sensitive communications. Major providers frequently scan messages for advertising purposes and store data in ways that leave users vulnerable to breaches and unauthorised access.
The following three email services represent a different approach—one that prioritizes user privacy through robust encryption, transparent practices and a genuine commitment to data protection. Each offers end-to-end encryption that ensures only intended recipients can read messages, while employing various technical and legal safeguards to keep user data secure from third-party access.
From Belgium's court-order requirements to Switzerland's strong privacy laws and Germany's open-source transparency, these services demonstrate how geography, technology and philosophy combine to create truly private communication platforms that put users back in control of their digital correspondence.
This encrypted email service launched in 2013 by ContactOffice Group operates from Brussels, Belgium, providing users with OpenPGP-based end-to-end encryption and digital signature capabilities whilst maintaining servers under Belgian privacy protection laws that require court approval for any access requests. The platform generates private keys within the browser and encrypts them using AES-256 with user passphrases, ensuring the service provider cannot access user encryption keys and supports standard security protocols including SPF, DKIM, DMARC, TLS and two-factor authentication alongside anti-spam filtering. Beyond secure email functionality that works with POP, IMAP, SMTP and Exchange ActiveSync protocols, the service integrates calendar management with CalDAV support, contact organisation with various import and export formats, document storage with online editing through WebDAV access and group collaboration tools for sharing files and calendars.
Founded in 2014 by CERN scientists and now majority-owned by the non-profit Proton Foundation, this Swiss technology company has built a comprehensive suite of privacy-focused services that serve over 100 million users worldwide. The company's flagship service, Proton Mail, offers end-to-end encryption for secure email communication, ensuring that only senders and recipients can read messages whilst the company itself cannot access the content.
The ecosystem has expanded to include Proton VPN for secure internet browsing, Proton Drive for encrypted cloud storage, Proton Calendar for scheduling, Proton Pass as an open-source password manager and recently Lumo, a privacy-centred AI chatbot. All services operate under Swiss privacy laws and employ zero-access encryption technology. The company's mission centres on protecting user privacy against both authoritarian surveillance and big technology companies' data collection practices, offering an alternative to advertisement-supported services that typically monetise user information.
Formerly known as Tutanota, this German-developed encrypted email service operates under a freemium model and was founded by Tutao GmbH in 2011, officially rebranding to Tuta in November 2023. The platform provides automatic end-to-end encryption for emails, subject lines, attachments and calendars using hybrid encryption including AES-256 and RSA-2048, whilst newer accounts benefit from post-quantum cryptography through the TutaCrypt protocol featuring algorithms like X25519 and Kyber-1024.
Registration requires no phone number and the service claims no IP logging, with private and public keys generated locally, and private keys encrypted by user passwords before storage. The open-source platform is accessible via webmail with applications for Android, iOS, Windows, macOS and Linux, and includes an encrypted calendar, contact storage, search functionality and two-factor authentication support. The service has received recognition for its strong encryption and privacy focus but has faced challenges including a significant drop in Google search visibility following Digital Markets Act implementation.
How complexity can blind you
17th August 2025Visitors may not have noticed it, but I was having a lot of trouble with this website. Intermittent slowdowns beset any attempt to add new content or perform any other administration. This was not happening on any other web portal that I had, even one sharing the same publishing software.
Even so, WordPress did get the blame at first, at least when deactivating plugins had no effect. Then, it was the turn of the web server, resulting in a move to something more powerful and my leaving Apache for Nginx at the same time. Redis caching was another suspect, especially when things got in a twist on the new instance. As if that were not enough, MySQL came in for some scrutiny too.
Finally, another suspect emerged: Cloudflare. Either some settings got mangled or something else was happening, but cutting out that intermediary was enough to make things fly again. Now, I use bunny.net for DNS duties instead, and the simplification has helped enormously; the previous layering was no help with debugging. With a bit of care, I might add some other tools behind the scenes while taking things slowly to avoid confusion in the future.
An alternate approach to setting up a local Git repository with a remote GitHub connection
24th March 2025For some reason, I ended with two versions of this at the draft stage, forcing me to compare and contrast before merging them together to produce what you find here. The inspiration was something that I encountered a while ago: getting a local repository set up in a perhaps unconventional manner.
The simpler way of working would be to set up a repo on GitHub and clone it to the local machine, yet other needs are the cause of doing things differently. In contrast, this scheme stars with initialising the local directory first using the following command after creating it with some content and navigating there:
git initThis marks the directory as a Git repository, allowing you to track changes by creating a hidden .git directory. Because security measures often require verification of directories when executing Git commands, it is best to configure a safe directory with the following command to avoid any issues:
git config --global --add safe.directory [path to directory]In the above, replace the path with your specific project directory. This ensures that Git recognises your directory as safe and authorised for operations, avoiding any messages whenever you work in there.
With that completed, it is time to add files to the staging area, which serves as an area where you can review and choose changes to be committed to the repository. Here are two commands that show different ways of accomplishing this:
git add README.md
git add .The first command stages the README.md file, preparing it for the next commit, while the second stages all files in the directory (the . is a wildcard operator that includes everything in there).
Once your files are staged, you are ready to commit them. A commit is essentially a snapshot of your changes, marking a specific point in the project's history. The following command will commit your staged changes:
git commit -m "first commit"The -m flag allows you to add a descriptive message for context; here, it is "first commit." This message helps with understanding the purpose of the commit when reviewing project history.
Before pushing your local files online, you will need to create an empty repository on GitHub using the GitHub website if you do not have one already. While still on the GitHub website, click on the Code button and copy the URL shown under the HTTPS tab that is displayed. This takes the form https://github.com/username/repository.gitand is required for running the next command in your local terminal session:
git remote add origin https://github.com/username/repository.gitThis command establishes a remote connection under the alias origin. By default, Git sets the branch name to 'master'. However, recent conventions prefer using 'main'. To rename your branch, execute:
git branch -M mainThis command will rename your current branch to 'main', aligning it with modern version control standards. Finally, you must push your changes from the local repository to the remote repository on GitHub, using the following command:
git push -u origin mainThe -u flag sets the upstream reference, meaning future push and pull operations will default to this remote branch. This last step completes the process of setting up a local repository, linking it to a remote one on GitHub, staging any changes, committing these and pushing them to the remote repository.