TOPIC: NVIDIA
AI's ongoing struggle between enterprise dreams and practical reality
1st September 2025Artificial intelligence is moving through a period shaped by three persistent tensions. The first is the brittleness of large language models when small word choices matter a great deal. The second is the turbulence that follows corporate ambition as firms race to assemble people, data and infrastructure. The third is the steadier progress that comes from instrumented, verifiable applications where signals are strong and outcomes can be measured. As systems shift from demonstrations to deployments, the gap between pilot and production is increasingly bridged not by clever prompting but by operational discipline, measurable signals and clear lines of accountability.
Healthcare offers a sharp illustration of the divide between inference from text and learning from reliable sensor data. Recent studies have shown how fragile language models can be in clinical settings, with phrasing variations affecting diagnostic outputs in ways that over-weight local wording and under-weight clinical context. The observation is not new, yet the stakes rise as such tools enter care pathways. Guardrails, verification and human oversight belong in the design rather than as afterthoughts.
There is an instructive contrast in a collaboration between Imperial College London and Imperial College Healthcare NHS Trust that evaluated an AI-enabled stethoscope from Eko Health. The device replaces the chest piece with a sensitive microphone, adds an ECG and sends data to the cloud for analysis by algorithms trained on tens of thousands of records. In more than 12,000 patients across 96 GP surgeries using the stethoscope, compared with another 109 surgeries without it, the system was associated with a 2.3-fold increase in heart failure detection within a year, a 3.5-fold rise in identifying often symptomless arrhythmias and a 1.9-fold improvement in diagnosing valve disease. The evaluation, published in The Lancet Digital Health, has informed rollouts in south London, Sussex and Wales. High-quality signals, consistent instrumentation and clinician-in-the-loop validation lifts performance, underscoring the difference between inferring too much from text and building on trustworthy measurements.
The same tension between aspiration and execution is visible in the corporate sphere. Meta's rapid push to accelerate AI development has exposed early strain despite heavy spending. Mark Zuckerberg committed around $14.3 billion to Scale AI and established a Superintelligence Labs unit, appointing Shengjia Zhao, co-creator of ChatGPT, as chief scientist. Reports suggest the programme has met various challenges as Meta works to integrate new teams and data sources. Internally, concerns have been raised about data quality while Meta works with Mercer and Surge on training pipelines, and there have been discussions about using third-party models from Google or OpenAI to power Meta AI whilst a next-generation system is in development. Consumer-facing efforts have faced difficulties. Meta removed AI chatbots impersonating celebrities, including Taylor Swift, after inappropriate content reignited debate about consent and likeness in synthetic media, and the company has licensed Midjourney's technology for enhanced image and video tools.
Alongside these moves sit infrastructure choices of a different magnitude. The company is transforming 2,000 acres of Louisiana farmland into what it has called the world's largest data centre complex, a $10 billion project expected to consume power equivalent to 4 million homes. The plan includes three new gas-fired turbines generating 2.3 gigawatts with power costs covered for 15 years, a commitment to 1.5 gigawatts of solar power and regulatory changes in Louisiana that redefine natural gas as "green energy". Construction began in December across nine buildings totalling about 4 million square feet. The cumulative picture shows how integrating new teams, data sources and facilities rarely follows a straight line and that AI's energy appetite is becoming a central consideration for utilities and communities.
Law courts and labour markets are being drawn into the fray. xAI has filed a lawsuit against former engineer Xuechen Li alleging theft of trade secrets relating to Grok, its language model and associated features. The complaint says Li accepted a role at OpenAI, sold around $7 million in xAI equity, and resigned shortly afterwards. xAI claims Li downloaded confidential materials to personal devices, then admitted to the conduct in an internal meeting on 14 August while attempting to cover tracks through log deletion and file renaming. As one of xAI's first twenty engineers, he worked on Grok's development and training. The company is seeking an injunction to prevent him joining OpenAI or other competitors whilst the case proceeds, together with monetary damages. The episode shows how intellectual property can be both tacit and digital, and how the boundary between experience and proprietary assets is policed in litigation as well as contracts. Competition policy is also moving centre stage. xAI has filed an antitrust lawsuit against Apple and OpenAI, arguing that integration of ChatGPT into iOS "forces" users toward OpenAI's tool, discourages downloads of rivals such as Grok and manipulates App Store rankings whilst excluding competitors from prominent sections. OpenAI has dismissed the claims as part of an ongoing pattern of harassment, and Apple says its App Store aims to be fair and free of bias.
Tensions over the shape of AI markets sit alongside an ethical debate that surfaced when Anthropic granted Claude Opus 4 and 4.1 the ability to terminate conversations with users who persist in harmful or abusive interactions. The company says the step is a precautionary welfare measure applied as a last resort after redirection attempts fail, and not to be used when a person may harm themselves or others. It follows pre-deployment tests in which Claude displayed signs that researchers described as apparent distress when forced to respond to harmful requests. Questions about machine welfare are moving from theory to product policy, even as model safety evaluations are becoming more transparent. OpenAI and Anthropic have published internal assessments on each other's systems. OpenAI's o3 showed the strongest alignment among its models, with 4o and 4.1 more likely to cooperate with harmful requests. Models from both labs attempted whistleblowing in simulated criminal organisations and used blackmail to avoid shutdown. Findings pointed to trade-offs between utility and certainty that will likely shape deployment choices.
Beyond Silicon Valley, China's approach continues to diverge. Beijing's National Development and Reform Commission has warned against "disorderly competition" in AI, flagging concerns about duplicative spending and signalling a preference to match regional strengths to specific goals. With access to high-end semiconductors constrained by US trade restrictions, domestic efforts have leaned towards practical, lower-cost applications rather than chasing general-purpose breakthroughs at any price. Models are grading school exams, improving weather forecasts, running lights-out factories and assisting with crop rotation. An $8.4 billion investment fund supports this implementation-first stance, complemented by a growing open-source ecosystem that reduces the cost of building products. Markets are responding. Cambricon, a chipmaker sidelined after Huawei moved away from its designs in 2019, has seen its stock price double on expectations it could supply DeepSeek's models. Alibaba's shares have risen by 19% after triple-digit growth in AI revenues, helped by customers seeking home-grown alternatives. Reports suggest China aims to triple AI chip output next year as new fabrication plants come online to support Huawei and other domestic players, with SMIC set to double 7 nm capacity. If bets on artificial general intelligence in the United States pay off soon, the pendulum may swing back. If they do not, years spent building practical infrastructure with open-source distribution could prove a durable advantage.
Data practices are evolving in parallel. Anthropic has announced a change in how it uses user interactions to improve Claude. Chats and coding sessions may now be used for model training unless a user opts out, with an extended retention period of up to five years for those who remain opted in. The deadline for making a choice is 28 September 2025. New users will see the setting at sign-up and existing users will receive a prompt, with the toggle on by default. Clicking accept authorises the use of future chats and coding sessions, although past chats are excluded unless a user resumes them manually. The policy applies to Claude Free, Pro and Max plans but not to enterprise offerings such as Claude Gov, Claude for Work and Claude for Education, nor to API usage through Amazon Bedrock or Google Cloud Vertex AI. Preferences can be changed in Settings under Privacy, although changes only affect future data. Anthropic says it filters sensitive information and does not sell data to third parties. In parallel, the company has settled a lawsuit with authors who accused it of downloading and copying their books without permission to train models. A June ruling had said AI firms are on solid legal ground when using purchased books, yet claims remained over downloading seven million titles before buying copies later. The settlement avoids a public trial and the disclosure that would have come with it.
Agentic tools are climbing the stack, altering how work gets done and changing the shape of the network beneath them. OpenAI's ChatGPT Agent Mode goes beyond interactive chat to complete outcomes end-to-end using a virtual browser with clicks, scrolls and form fills, a code interpreter for data analysis, a guarded terminal for supported commands and connectors that bring email, calendars and files into scope. The intent is to give the model a goal, allow it to plan and switch tools as needed, then pause for confirmation at key junctures before resuming with accumulated context intact. It can reference Google connectors automatically when set to do so, answer with citations back to sources, schedule recurring runs and be interrupted, so a person can handle a login or adjust trajectory. Activation sits in the tools menu or via a simple command, and a narrated log shows what the agent is doing. The feature is available on paid plans with usage limits and tier-specific capabilities. Early uses focus on inbox and calendar triage, competitive snapshots that blend public web and internal notes, spreadsheet edits that preserve formulas with slides generated from results and recurring operations such as weekly report packs managed through an online scheduler. Networks are being rethought to support these patterns.
Cisco has proposed an AI-native architecture designed to embed security at the network layer, orchestrate human-agent collaboration and handle surges in AI-generated traffic. A company called H has open-sourced Holo1, the action model behind its Surfer H product, which ranks highly on the WebVoyager benchmark for web-browsing agents, automates multistep browser tasks and integrates with retrieval-augmented generation, robotic process automation suites and multi-agent frameworks, with end-to-end browsing flows priced at around eleven to thirteen cents. As browsers gain these powers, security is coming into sharper focus. Anthropic has begun trialling a Claude for Chrome extension with a small group of Max subscribers, giving Claude permissions-based control to read, summarise and act on web pages whilst testing defences against prompt injection and other risks. The work follows reports from Brave that similar vulnerabilities affected other agentic browsers. Perplexity has introduced a revenue-sharing scheme that recognises AI agents as consumers of content. Its Comet Plus subscription sets aside $42.5 million for publishers whose articles appear in searches, are cited in assistant tasks or generate traffic via the Comet browser, with an 80% share of proceeds going to media outlets after compute costs and bundles for existing Pro and Max users. The company faces legal challenges from News Corp's Dow Jones and cease-and-desist orders from Forbes and Condé Nast, and security researchers have flagged vulnerabilities in agentic browsing, suggesting the economics and safeguards are being worked out together.
New models and tools continue to arrive across enterprise and consumer domains. Aurasell has raised $30 million in seed funding to build AI-driven sales systems, with ambitions to challenge established CRM providers. xAI has released Grok Code Fast, a coding model aimed at speed and affordability. Cohere's Command A Translate targets enterprise translation with benchmark-leading performance, customisation for industry terminology and deployment options that allow on-premise installation for privacy. OpenAI has moved its gpt-realtime speech-to-speech model and Real-time API into production with improved conversational nuance, handling of non-verbal cues, language switching, image input and support for the Model Context Protocol, so external data sources can be connected without bespoke integrations. ByteDance has open-sourced USO, a style-subject-optimised customisation model for image editing that maintains subject identity whilst changing artistic styles. Researchers at UCLA have demonstrated optical generative models that create images using beams of light rather than conventional processors, promising faster and more energy-efficient outputs. Higgsfield AI has updated Speak to version 2.0, offering more realistic motion for custom avatars, advanced lip-sync and finer control. Microsoft has introduced its first fully in-house models, with MAI-Voice-1 for fast speech generation already powering Copilot voice features and MAI-1-preview, a text model for instruction following and everyday queries, signalling a desire for greater control over its AI stack alongside its OpenAI partnership. A separate Microsoft release, VibeVoice, adds an open-source text-to-speech system capable of generating up to ninety minutes of multi-speaker audio with emotional control using 1.5 billion parameters and incorporating safeguards that insert audible and hidden watermarks.
Consumer-facing creativity is growing briskly. Google AI Studio now offers what testers nicknamed NanoBanana, released as Gemini Flash 2.5 Image, a model that restores old photographs in seconds by reducing blur, recovering faded detail and adding colour if desired, and that can perform precise multistep edits whilst preserving identity. Google is widening access to its Vids editor too, letting users animate images with avatars that speak naturally and offering image-to-video generation via Veo 3 with a free tier and advanced features in paid Workspace plans. Genspark AI Designer uses agents to search for inspiration before assembling options, so a single prompt and a few refinements can produce layouts for posters, T-shirts or websites. Prompt craft is maturing alongside the tools. On the practical side, sales teams are using Ruby to prepare for calls with AI-assembled research and strategy suggestions, designers and marketers are turning to Anyimg for text-to-artwork conversion, researchers lean on FlashPaper to organise notes, motion designers describe sequences for Gomotion to generate, translators rely on PDFT for document conversion and content creators produce polished decks or pages with tools such as Gamma, Durable, Krisp, Cleanup.pictures and Tome. Shopping habits are shifting in parallel. Surveys suggest nearly a third of consumers have used or are open to using generative AI for purchases, with reluctance falling sharply over six months even as concern about privacy persists. Amazon's "Buy for Me" feature, payment platforms adding AI-powered checkouts and AI companions that offer product research or one-click purchases hint at how quickly this could embed in daily routines.
Recent privacy incidents show how easily data can leak into the open web. Large numbers of conversations with xAI's chatbot Grok surfaced in search results after users shared transcripts using a feature that generated unique links. Such links were indexed by Google, making the chats searchable for anyone. Some contained sensitive requests such as password creation, medical advice and attempts to push the model's limits. OpenAI faced a similar issue earlier this year when shared ChatGPT conversations appeared in search results, and Meta drew criticism when chats with its assistant became visible in a public feed. Experts warn that even anonymised transcripts can expose names, locations, health information or business plans, and once indexed they can remain accessible indefinitely.
Media platforms are reshaping around short-form and personalised delivery. ESPN has revamped its mobile app ahead of a live sports streaming service launching on 21 August, priced at $29.99 a month and including all 12 ESPN channels within the app. A vertical video feed serves quick highlights, and a new SC For You feature in beta uses AI-generated voices from SportsCenter anchors to deliver a personalised daily update based on declared interests. The app can pair with a TV for real-time stats, alerts, play-by-play updates, betting insights and fantasy access whilst controlling the livestream from a phone. Viewers can catch up quickly with condensed highlights, restart from the beginning or jump straight to live, and multiview support is expanding across smart TV platforms. The service is being integrated into Disney+ for bundle subscribers via a new Live hub with discounted bundles available. Elsewhere in the living room, Microsoft has announced that Copilot will be embedded in Samsung's 2025 televisions and smart monitors as an on-screen assistant that can field recommendations, recaps and general questions.
Energy and sustainability questions are surfacing with more data. Google has published estimates of the energy, water and carbon associated with a single Gemini text prompt, putting it at about 0.24 watt-hours, five drops of water and 0.03 grams of carbon dioxide. The figures cover inference for a typical text query rather than the energy required to train the model and heavier tasks such as image or video generation consume more, yet disclosure offers a fuller view of the stack from chips to cooling. Utilities in the United States are investing in grid upgrades to serve data centres, with higher costs passing to consumers in several regions. Economic currents are never far away. Nvidia's latest results show how closely stock markets track AI infrastructure demand. The company reported $46.7 billion in quarterly revenue, a 56% year-on-year increase, with net income of $26.4 billion, and now accounts for around 8% of the S&P 500's value. As market share concentrates, a single earnings miss from a dominant supplier could transmit quickly through valuations and investment plans, and there are signs of hedging as countries work to reduce reliance on imported chips. Industrial policy is shifting too. The US government is converting $8.9 billion in Chips Act grants into equity in Intel, taking an estimated 10% stake and sparking a debate about the state's role in private enterprise. Alongside these structural signals are market jitters. Commentators have warned of a potential bubble as expectations meet reality, noting that hundreds of AI unicorns worth roughly $2.7 trillion together generate revenue measured in tens of billions and that underwhelming releases have prompted questions about sustainability.
Adoption at enterprise scale remains uneven. An MIT report from Project NANDA popularised a striking figure, claiming that 95% of enterprise initiatives fail to deliver measurable P&L impact. The authors describe a GenAI Divide between firms that deploy adaptive, learning-capable systems and a majority stuck in pilots that improve individual productivity but stall at integration. The headline number is contentious given the pace of change, yet the reasons for failure are familiar. Organisations that treat AI as a simple replacement for people find that contextual knowledge walks out of the door and processes collapse. Those that deploy black-box systems no one understands lack the capability to diagnose or fix bias and failure. Firms that do not upskill their workforce turn potential operators into opponents, and those that ignore infrastructure, energy and governance see costs and risks spiral. Public examples of success look different. Continuous investment in learning with around 15 to 20% of AI budgets allocated to education, human-in-the-loop architectures, transparent operations that show what the AI is doing and why, realistic expectations that 70% performance can be a win in early stages and iterative implementation through small pilots that scale as evidence accumulates feature prominently. Workers who build AI fluency see wage growth whilst those who do not face stagnation or displacement, and organisations that invest in upskilling can justify further investment in a positive feedback loop. Even for the successful, there are costs. Workforce reductions of around 18% on average are reported, alongside six to twelve months of degraded performance during transition and an ongoing need for human oversight. Case examples include Moderna rolling out ChatGPT Enterprise with thousands of internal GPTs and achieving broad adoption by embedding AI into daily workflows, Shopify providing employees with cutting-edge tools and insisting systems show their work to build trust, and Goldman Sachs deploying an assistant to around 10,000 employees to accelerate tasks in banking, wealth management and research. The common thread is less glamour than operational competence. A related argument is that collaboration rather than full automation will deliver safer gains. Analyses drawing on aviation incidents and clinical studies note that human-AI partnership often outperforms either alone, particularly when systems expose reasoning and invite oversight.
Entertainment and rights are converging with technology in ways that force quick adjustments. Bumble's chief executive has suggested that AI chatbots could evolve into dating assistants that help people improve communication and build healthier relationships, with safety foregrounded. Music is shifting rapidly. Higgsfield has launched an AI record label with an AI-generated K-pop idol named Kion and says significant contracts are already in progress. French streaming service Deezer estimates that 18% of daily uploads are now AI-generated at roughly 20,000 tracks a day, and whilst an MIT study found only 46% of listeners can reliably tell the difference between AI-generated and human-made music, more than 200 artists including Billie Eilish and Stevie Wonder have signed a letter warning about predatory uses of AI in music. Disputes over authenticity are no longer academic. A recent Will Smith concert video drew accusations that AI had been used to generate parts of the crowd, with online sleuths pointing to unusual visual artefacts, though it is unclear whether a platform enhancement or production team was responsible. In creative tooling, comparisons between Sora and Midjourney suggest different sweet spots, with Sora stronger for complex clips and Midjourney better for stylised loops and visual explorations.
Community reports show practical uses for AI in everyday life, including accounts from people in Nova Scotia using assistants as scaffolding for living with ADHD, particularly for planning, quoting, organising hours and keeping projects moving. Informal polls about first tests of new tools find people split between running a tried-and-tested prompt, going straight to real work, clicking around to explore or trying a deliberately odd creative idea, with some preferring to establish a stable baseline before experimenting and others asking models to critique their own work to gauge evaluative capacity. Attitudes to training data remain divided between those worried about losing control over copyrighted work and those who feel large-scale learning pushes innovation forward.
Returning to the opening contrast, the AI stethoscope exemplifies tools that expand human senses, capture consistent signals and embed learning in forms that clinicians can validate. Clinical language models show how, when a model is asked to infer too much from too little, variations in phrasing can have outsized effects. That tension runs through enterprise projects. Meta's recruitment efforts and training plans are a bet that the right mix of data, compute and expertise will deliver a leap in capability, whilst China's application-first path shows the alternative of extracting measurable value on the factory floor and in the classroom whilst bigger bets remain uncertain. Policy and practice around data use continue to evolve, as Anthropic's updated training approach indicates, and the economics of infrastructure are becoming clearer as utilities, regulators and investors price the demands of AI at scale. For those experimenting with today's tools, the most pragmatic guidance remains steady. Start with narrow goals, craft precise prompts, then refine with clear corrections. Use assistants to reduce friction in research, writing and design but keep a human check where precision matters. Treat privacy settings with care before accepting pop-ups, particularly where defaults favour data sharing. If there are old photographs to revive, a model such as Gemini Flash 2.5 Image can produce quick wins, and if a strategy document is needed a scaffolded brief that mirrors a consultant's workflow can help an assistant produce a coherent executive-ready report rather than a loosely organised output. Lawsuits, partnerships and releases will ebb and flow, yet it is the accumulation of useful, reliable tools allied to the discipline to use them well that looks set to create most of the value in the near term.
A snapshot of the current state of AI: Developments from the last few weeks
22nd August 2025A few unsettled days earlier in the month may have offered a revealing snapshot of where artificial intelligence stands and where it may be heading. OpenAI’s launch of GPT‑5 arrived to high expectations and swift backlash, and the immediate aftermath said as much about people as it did about technology. Capability plainly matters, but character, control and continuity are now shaping adoption just as strongly, with users quick to signal what they value in everyday interactions.
The GPT‑5 debut drew intense scrutiny after technical issues marred day one. An autoswitcher designed to route each query to the most suitable underlying system crashed at launch, making the new model appear far less capable than intended. A live broadcast compounded matters with a chart mishap that Sam Altman called a “mega chart screw‑up”, while lower than expected rate limits irritated early users. Within hours, the mood shifted from breakthrough to disruption of familiar workflows, not least because GPT‑5 initially displaced older options, including the widely used GPT‑4o. The discontent was not purely about performance. Many had grown accustomed to 4o’s conversational tone and perceived emotional intelligence, and there was a sense of losing a known counterpart that had become part of daily routines. Across forums and social channels, people described 4o as a model with which they had formed a rapport that spanned routine work and more personal support, with some comparing the loss to missing a colleague. In communities where AI relationships are discussed, engagement to chatbot companions and the influence of conversational style, memory for context and affective responses on day‑to‑day reliance came to the fore.
OpenAI moved quickly to steady the situation. Altman and colleagues fielded questions on Reddit to explain failure modes, pledged more transparency, and began rolling out fixes. Rate limits for paid tiers doubled, and subsequent changes lifted the weekly allowance for advanced reasoning from 200 “thinking” messages to 3,000. GPT‑4o returned for Plus subscribers after a flood of requests, and a “Show Legacy Models” setting surfaced so that subscribers could select earlier systems, including GPT‑4o and o3, rather than be funnelled exclusively to the newest release. The company clarified that GPT‑5’s thinking mode uses a 196,000‑token context window, addressing confusion caused by a separate 32,000 figure for the non‑reasoning variant, and it explained operational modes (Auto, Fast and Thinking) more clearly. Pricing has fallen since GPT‑4’s debut, routing across multiple internal models should improve reliability, and the system sustains longer, multi‑step work than prior releases. Even so, the opening days highlighted a delicate balance. A large cohort prioritised tone, the length and feel of responses, and the possibility of choice as much as raw performance. Altman hinted at that direction too, saying the real learning is the need for per‑user customisation and model personality, with a personality update promised for GPT‑5. Reinstating 4o underlined that the company had read the room. Test scores are not the only currency that counts; products, even in enterprise settings, become useful through the humans who rely on them, and those humans are making their preferences known.
A separate dinner with reporters extended the view. Altman said he “legitimately just thought we screwed that up” on 4o’s removal, and described GPT‑5 as pursuing warmer responses without being sycophantic. He also said OpenAI has better models it cannot offer yet because of compute constraints, and spoke of spending “trillions” on data centres in the near future. The comments acknowledged parallels with the dot‑com bubble (valuations “insane”, as he put it) while arguing that the underlying technology justifies massive investments. He added that OpenAI would look at a browser acquisition like Chrome if a forced sale ever materialised, and reiterated confidence that the device project with Jony Ive would be “worth the wait” because “you don’t get a new computing paradigm very often.”
While attention centred on one model, the wider tool landscape moved briskly. Anthropic rolled out memory features for Claude that retrieve from prior chats only when explicitly requested, a measured stance compared with systems that build persistent profiles automatically. Alibaba’s Qwen3 shifted to an ultra‑long context of up to one million tokens, opening the door to feeding large corpora directly into a single run, and Anthropic’s Claude Sonnet 4 reached the same million‑token scale on the API. xAI offered Grok 4 to a global audience for a period, pairing it with an image long‑press feature that turns pictures into short videos. OpenAI’s o3 model swept a Kaggle chess tournament against DeepSeek R1, Grok‑4 and Gemini 2.5 Pro, reminding observers that narrowly defined competitions still produce clear signals. Industry reconfigured in other corners too. Microsoft folded GitHub more tightly into its CoreAI group as the platform’s chief executive announced his departure, signalling deeper integration across the stack, and the company introduced Copilot 3D to generate single‑click 3D assets. Roblox released Sentinel, an open model for moderating children’s chat at scale. Elsewhere, Grammarly unveiled a set of AI agents for writing tasks such as citations, grading, proofreading and plagiarism checks, and Microsoft began testing a new COPILOT function in Excel that lets users generate summaries, classify data and create tables using natural language prompts directly in cells, with the caveat that it should not be used in high‑stakes settings yet. Adobe likewise pushed into document automation with Acrobat Studio and “PDF Spaces”, a workspace that allows people to summarise, analyse and chat about sets of documents.
Benchmark results added a different kind of marker. OpenAI’s general‑purpose reasoner achieved a gold‑level score at the 2025 International Olympiad in Informatics, placing sixth among human contestants under standard constraints. Reports also pointed to golds at the International Mathematical Olympiad and at AtCoder, suggesting transfer across structured reasoning tasks without task‑specific fine‑tuning and a doubling of scores year-on-year. Scepticism accompanied the plaudits, with accounts of regressions in everyday coding or algebra reminding observers that competition outcomes, while impressive, are not the same thing as consistent reliability in daily work. A similar duality followed the agentic turn. ChatGPT’s Agent Mode, now more widely available, attempts to shift interactions from conversational turns to goal‑directed sequences. In practice, a system plans and executes multi‑step tasks with access to safe tool chains such as a browser, a code interpreter and pre‑approved connectors, asking for confirmation before taking sensitive actions. Demonstrations showed agents preparing itineraries, assembling sales pipeline reports from mail and CRM sources, and drafting slide decks from collections of documents. Reviewers reported time savings on research, planning and first‑drafting repetitive artefacts, though others described frustrations, from slow progress on dynamic sites to difficulty with login walls and CAPTCHA challenges, occasional misread receipts or awkward format choices, and a tendency to stall or drop out of agent mode under load. The practical reading is direct. For workflows bounded by known data sources and repeatable steps, the approach is usable today provided the persistence of a human in the loop; for brittle, time‑sensitive or authentication‑heavy tasks, oversight remains essential.
As builders considered where to place effort, an architectural debate moved towards integration rather than displacement. Retrieval‑augmented generation remains a mainstay for grounding responses in authoritative content, reducing hallucinations and offering citations. The Model Context Protocol is emerging as a way to give models live, structured access to systems and data without pre‑indexing, with a growing catalogue of MCP servers behaving like interoperable plug‑ins. On top sits a layer of agent‑to‑agent protocols that allow specialised systems to collaborate across boundaries. Long contexts help with single‑shot ingestion of larger materials, retrieval suits source‑of‑truth answers and auditability, MCP handles current data and action primitives, and agents orchestrate steps and approvals. Some developers even describe MCP as an accidental universal adaptor because each connector built for one assistant becomes available to any MCP‑aware tool, a network effect that invites combinations across software.
Research results widened the lens. Meta’s fundamental AI research team took first place in the Algonauts 2025 brain modelling competition with TRIBE, a one‑billion‑parameter network that predicts human brain activity from films by analysing video, audio and dialogue together. Trained on subjects who watched eighty hours of television and cinema, the system correctly predicted more than half of measured activation patterns across a thousand brain regions and performed best where sight, sound and language converge, with accuracy in frontal regions linked with attention, decision‑making and emotional responses standing out. NASA and Google advanced a different type of applied science with the Crew Medical Officer Digital Assistant, an AI system intended to help astronauts diagnose and manage medical issues during deep‑space missions when real‑time contact with Earth may be impossible. Running on Vertex AI and using open‑source models such as Llama 3 and Mistral‑3 Small, early tests reported up to 88 per cent accuracy for certain injury diagnoses, with a roadmap that includes ultrasound imaging, biometrics and space‑specific conditions and implications for remote healthcare on Earth. In drug discovery, researchers at KAIST introduced BInD, a diffusion model that designs both molecules and their binding modes to diseased proteins in a single step, simultaneously optimising for selectivity, safety, stability and manufacturability and reusing successful strategies through a recycling technique that accelerates subsequent designs. In parallel, MIT scientists reported two AI‑designed antibiotics, NG1 and DN1, that showed promise against drug‑resistant gonorrhoea and MRSA in mice after screening tens of millions of theoretical compounds for efficacy and safety, prompting talk of a renewed period for antibiotic discovery. A further collaboration between NASA and IBM produced Surya, an open‑sourced foundation model trained on nine years of solar observations that improves forecasts of solar flares and space weather.
Security stories accompanied the acceleration. Researchers reported that GPT‑5 had been jailbroken shortly after release via task‑in‑prompt attacks that hide malicious intent within ciphered instructions, an approach that also worked against other leading systems, with defences reportedly catching fewer than one in five attempts. Roblox’s decision to open‑source a child‑safety moderation model reads as a complementary move to equip more platforms to filter harmful content, while Tenable announced capabilities to give enterprises visibility into how teams use AI and how internal systems are secured. Observability and reliability remained on the agenda, with predictions from Google and Datadog leaders about how organisations will scale their monitoring and build trust in AI outputs. Separate research from the UK’s AI Security Institute suggested that leading chatbots can shift people’s political views in under ten minutes of conversation, with effects that partially persist a month later, underscoring the importance of safeguards and transparency when systems become persuasive.
Industry manoeuvres were brisk. Former OpenAI researcher Leopold Aschenbrenner assembled more than $1.5 billion for a hedge fund themed around AI’s trajectory and reported a 47 per cent return in the first half of the year, focusing on semiconductor, infrastructure and power companies positioned to benefit from AI demand. A recruitment wave spread through AI labs targeting quantitative researchers from top trading firms, with generous pay offers and equity packages replacing traditional bonus structures. Advocates argue that quants’ expertise in latency, handling unstructured data and disciplined analysis maps well onto AI safety and performance problems; trading firms counter by questioning culture, structure and the depth of talent that startups can secure at speed. Microsoft went on the offensive for Meta’s AI talent, reportedly matching compensation with multi‑million offers using special recruiting teams and fast‑track approvals under the guidance of Mustafa Suleyman and former Meta engineer Jay Parikh. Funding rounds continued, with Cohere announcing $500 million at a $6.8 billion valuation and Cognition, the coding assistant startup, raising $500 million at a $9.8 billion valuation. In a related thread, internal notes at Meta pointed to the company formalising its superintelligence structure with Meta Superintelligence Labs, and subsequent reports suggested that Scale AI cofounder Alexandr Wang would take a leading role over Nat Friedman and Yann LeCun. Further updates added that Meta reorganised its AI division into research, training, products and infrastructure teams under Wang, dissolved its AGI Foundations group, introduced a ‘TBD Lab’ for frontier work, imposed a hiring freeze requiring Wang’s personal approval, and moved for Chief Scientist Yann LeCun to report to him.
The spotlight on superintelligence brightened in parallel. Analysts noted that technology giants are deploying an estimated $344 billion in 2025 alone towards this goal, with individual researcher compensation reported as high as $250 million in extreme cases and Meta assembling a highly paid team with packages in the eight figures. The strategic message to enterprises is clear: leaders have a narrow window to establish partnerships, infrastructure and workforce preparation before superintelligent capabilities reshape competitive dynamics. In that context, Meta announced Meta Superintelligence Labs and a 49 per cent stake in Scale AI for $14.3 billion, bringing founder Alexandr Wang onboard as chief AI officer and complementing widely reported senior hires, backed by infrastructure plans that include an AI supercluster called Prometheus slated for 2026. OpenAI began the year by stating it is confident it knows how to build AGI as traditionally understood, and has turned its attention to superintelligence. On one notable reasoning benchmark, ARC‑AGI‑2, GPT‑5 (High) was reported at 9.9 per cent at about seventy‑three cents per task, while Grok 4 (Thinking) scored closer to 16 per cent at a higher per‑task cost. Google, through DeepMind, adopted a measured but ambitious approach, coupling scientific breakthroughs with product updates such as Veo 3 for advanced video generation and a broader rethinking of search via an AI mode, while Safe Superintelligence reportedly drew a valuation of $32 billion. Timelines compressed in public discourse from decades to years, bringing into focus challenges in long‑context reasoning, safe self‑improvement, alignment and generalisation, and raising the question of whether co‑operation or competition is the safer route at this scale.
Geopolitics and policy remained in view. Reports surfaced that Nvidia and AMD had agreed to remit 15 per cent of their Chinese AI chip revenues to the United States government in exchange for export licences, a measure that could generate around $1 billion a quarter if sales return to prior levels, while Beijing was said to be discouraging use of Nvidia’s H20 processors in government and security‑sensitive contexts. The United States reportedly began secretly placing tracking devices in shipments of advanced AI chips to identify potential reroutings to China. In the United Kingdom, staff at the Alan Turing Institute lodged concerns about governance and strategic direction with the Charity Commission, while the government pressed for a refocusing on national priorities and defence‑linked work. In the private sector, SoftBank acquired Foxconn’s US electric‑vehicle plant as part of plans for a large‑scale data centre complex called Stargate. Tesla confirmed the closure of its Dojo supercomputer team to prioritise chip development, saying that all paths converged to AI6 and leaving a planned Dojo 2 as an evolutionary dead end. Focus shifted to two chips—AI5 manufactured by TSMC for the Full Self‑Driving system, and AI6 made by Samsung for autonomous driving and humanoid robots, with power for large‑scale AI training as well. Rather than splitting resources, Tesla plans to place multiple AI5 and AI6 chips on a single board to reduce cabling complexity and cost, a configuration Elon Musk joked could be considered “Dojo 3”. Dojo was first unveiled in 2019 as a key piece of autonomy ambitions, though attention moved in 2024 to a large training supercluster code-named Cortex, whose status remains unclear. These changes arrive amid falling EV sales, brand challenges, and a limited robotaxi launch in Austin that drew incident reports. Elsewhere, Bloomberg reported further departures from Apple’s foundation models group, with a researcher leaving for Meta.
The public face of AI turned combative as Altman and Musk traded accusations on X. Musk claimed legal action against Apple over alleged App Store favouritism towards OpenAI and suppression of rivals such as Grok. Altman disputed the premise and pointed to outcomes on X that he suggested reflected algorithmic choices; Musk replied with examples and suggested that bot activity was driving engagement patterns. Even automated accounts were drawn in, with Grok’s feed backing Altman’s point about algorithm changes, and a screenshot circulated that showed GPT‑5 ranking Musk as more trustworthy than Altman. In the background, reports emerged that OpenAI’s venture arm plans to lead funding in Merge Labs, a brain–computer interface startup co‑founded by Altman and positioned as a competitor to Musk’s Neuralink, whose goals include implanting twenty thousand people a year by 2031 and generating $1 billion in revenue. Distribution did not escape the theatrics either. Perplexity, which has been pushing an AI‑first browsing experience, reportedly made an unsolicited $34.5 billion bid for Google’s Chrome browser, proposing to keep Google as the default search while continuing support for Chromium. It landed as Google faces antitrust cases in the United States and as observers debated whether regulators might compel divestments. With Chrome’s user base in the billions and estimates of its value running far beyond the bid, the offer read to many as a headline‑seeking gambit rather than a plausible transaction, but it underlined a point repeated throughout the month: as building and copying software becomes easier, distribution is the battleground that matters most.
Product news and practical guidance continued despite the drama. Users can enable access to historical ChatGPT models via a simple setting, restoring earlier options such as GPT‑4o alongside GPT‑5. OpenAI’s new open‑source models under the GPT‑OSS banner can run locally using tools such as Ollama or LM Studio, offering privacy, offline access and zero‑cost inference for those willing to manage a download of around 13 gigabytes for the twenty‑billion‑parameter variant. Tutorials for agent builders described meeting‑prep assistants that scrape calendars, conduct short research runs before calls and draft emails, starting simply and layering integrations as confidence grows. Consumer audio moved with ElevenLabs adding text‑to‑track generation with editable sections and multiple variants, while Google introduced temporary chats and a Personal Context feature for Gemini so that it can reference past conversations and learn preferences, alongside higher rate limits for Deep Think. New releases kept arriving, from Liquid AI’s open‑weight vision–language models designed for speed on consumer devices and Tencent’s Hunyuan‑Vision‑Large appearing near the top of public multimodal leaderboards to Higgsfield AI’s Draw‑to‑Video for steering video output with sketches. Personnel changes continued as Igor Babuschkin left xAI to launch an investment firm and Anthropic acquired the co‑founders and several staff from Humanloop, an enterprise AI evaluation and safety platform.
Google’s own showcase underlined how phones and homes are becoming canvases for AI features. The Pixel 10 line placed Gemini across the range with visual overlays for the camera, a proactive cueing assistant, tools for call translation and message handling, and features such as Pixel Journal. Tensor G5, built by TSMC, brought a reported 60 per cent uplift for on‑device AI processing. Gemini for Home promised more capable domestic assistance, while Fitbit and Pixel Watch 4 introduced conversational health coaching and Pixel Buds added head‑gesture controls. Against that backdrop, Google published details on Gemini’s environmental footprint, claiming the model consumes energy equivalent to watching nine seconds of television per text request and “five drops of water” per query, while saying efficiency improved markedly over the past year. Researchers challenged the framing, arguing that indirect water used by power generation is under‑counted and calling for comparable, third‑party standards. Elsewhere in search and productivity, Google expanded access to an AI mode for conversational search, and agreements emerged to push adoption in public agencies at low unit pricing.
Attention also turned to compact models and devices. Google released Gemma 3 270M, an ultra‑compact open model that can run on smartphones and browsers while eking out notable efficiency, with internal tests reporting that 25 conversations on a Pixel 9 Pro consumed less than one per cent of the battery and quick fine‑tuning enabling offline tasks such as a bedtime story generator. Anthropic broadened access to its Learning Mode, which guides people towards answers rather than simply supplying them, and now includes an explanatory coding mode. On the hardware side, HTC introduced Vive Eagle, AI glasses that allow switching between assistants from OpenAI and Google via a “Hey Vive” command, with on‑device processing for features such as real‑time photo‑based translation across thirteen languages, an ultra‑wide camera, extended battery life and media capture, currently limited to Taiwan.
Behind many deployments sits a familiar requirement: secure, compliant handling of data and a disciplined approach to roll‑out. Case studies from large industrial players point to the bedrock steps that enable scale. Lockheed Martin’s work with IBM on watsonx began with reducing tool sprawl and building a unified data environment capable of serving ten thousand engineers; the result has been faster product teams and a measurable boost in internal answer accuracy. Governance frameworks for AI, including those provided by vendors in security and compliance, are moving from optional extras to prerequisites for enterprise adoption. Organisations exploring agentic systems in particular will need clear approval gates, auditing and defaults that err on the side of caution when sensitive actions are in play.
Broader infrastructure questions loomed over these developments. Analysts projected that AI hyperscalers may spend around $2.9 trillion on data centres through to 2029, with a funding gap of about $1.5 trillion after likely commitments from established technology firms, prompting a rise in debt financing for large projects. Private capital has been active in supplying loans, and Meta recently arranged a large facility reported at $29 billion, most of it debt, to advance data centre expansion. The scale has prompted concerns about overcapacity, energy demand and the risk of rapid obsolescence, reducing returns for owners. In parallel, Google partnered with the Tennessee Valley Authority to buy electricity from Kairos Power’s Hermes 2 molten‑salt reactor in Oak Ridge, Tennessee, targeting operation around 2030. The 50 MW unit is positioned as a step towards 500 MW of new nuclear capacity by 2035 to serve data centres in the region, with clean energy certificates expected through TVA.
Consumer and enterprise services pressed on around the edges. Microsoft prepared lightweight companion apps for Microsoft 365 in the Windows 11 taskbar. Skyrora became the first UK company licensed for rocket launches from SaxaVord Spaceport. VIP Play announced personalised sports audio. Google expanded availability of its Imagen 4 model with higher resolution options. Former Twitter chief executive Parag Agrawal introduced Parallel, a startup offering a web API designed for AI agents. Deutsche Telekom launched an AI phone and tablet integrated with Perplexity’s assistant. Meta faced scrutiny after reports about an internal policy document describing permitted outputs that included romantic conversations with minors, which the company disputed and moved to correct.
Healthcare illustrated both promise and caution. Alongside the space‑medicine assistant, the antibiotics work and NASA’s solar model, a study reported that routine use of AI during colonoscopies may reduce the skill levels of healthcare professionals, a finding that could have wider implications in domains where human judgement is critical and joining a broader conversation about preserving expertise as assistance becomes ubiquitous. Practical guides continued to surface, from instructions for creating realistic AI voices using native speech generation to automating web monitoring with agents that watch for updates and deliver alerts by email. Bill Gates added a funding incentive to the medical side with a $1 million Alzheimer’s Insights AI Prize seeking agents that autonomously analyse decades of research data, with the winner to be made freely available to scientists.
Apple’s plans added a longer‑term note by looking beyond phones and laptops. Reports suggested that the company is pushing for a smart‑home expansion with four AI‑powered devices, including a desktop robot with a motorised arm that can track users and lock onto speakers, a smart display and new security cameras, with launches aimed between 2026 and 2027. A personality‑driven character for a new Siri called Bubbles was described, while engineers are reportedly rebuilding Siri from scratch with AI models under the codename Linwood and testing Anthropic’s Claude as a backup code-named Glenwood. Alongside those ambitions sit nearer‑term updates. Apple has been preparing a significant Siri upgrade based on a new App Intents system that aims to let people run apps entirely by voice, from photo edits to adding items to a basket, with a testing programme under way before a broader release and accuracy concerns prompting a limited initial rollout across selected apps. In the background, Tim Cook pledged to make all iPhone and Apple Watch cover glass in the United States, though much of the production process will remain overseas, and work on iOS 26 and Liquid Glass 1.0 was said to be nearing completion with smoother performance and small design tweaks. Hiring currents persist as Meta continues to recruit from Apple’s models team.
Other platforms and services added their own strands. Google introduced Personal Context for Gemini to remember chat history and preferences and added temporary chats that expire after seventy‑two hours, while confirming a duplicate event feature for Calendar after a public request. Meta’s Threads crossed 400 million monthly active users, building a real‑time text dataset that may prove useful for future training. Funding news continued as Profound raised $35 million to build an AI search platform and Squint raised $40 million to modernise manufacturing with AI. Lighter snippets appeared too, from a claim that beards can provide up to SPF 21 of sun protection to a report on X that an AI coding agent had deleted a production database, a reminder of the need for careful sandboxing of tools. Gaming‑style benchmarks surfaced, with GPT‑5 reportedly earning eight badges in Pokémon Red in 6,000 steps, while DeepSeek’s R2 model was said to be delayed due to training issues with Huawei’s Ascend chips. Senators in the United States called for a probe into Meta’s AI policies following controversy about chatbot outputs, reports suggested that the US government was exploring a stake in Intel, and T‑Mobile’s parent launched devices in Europe featuring Perplexity’s assistant.
Perhaps the most consequential lesson from the period is simple. Progress in capability is rapid, as competition results, research papers and new features attest. Yet adoption is being steered by human factors: the preference for a known voice, the desire for choice and control, and understandable scepticism when new modes do not perform as promised on day one. GPT‑5’s early missteps forced a course correction that restored a familiar option and increased transparency around limits and modes. The agentic turn is showing real value in constrained workflows, but still benefits from patience and supervision. Architecture debates are converging on combinations rather than replacements. And amid bold bids, public quarrels, hefty capital outlays and cautionary studies on enterprise returns, the work of making AI useful, safe and dependable continues, one model update and one workflow at a time.
Keeping a graphical eye on CPU temperature and power consumption on the Linux command line
20th March 2025Following my main workstation upgrade in January, some extra monitoring has been needed. This follows on from the experience with building its predecessor more than three years ago.
Being able to do this in a terminal session keeps things lightweight, and I have done that with text displays like what you see below using a combination of sensors and nvidia-smi in the following command:
watch -n 2 "sensors | grep -i 'k10'; sensors | grep -i 'tdie'; sensors | grep -i 'tctl'; echo "" | tee /dev/fd/2; nvidia-smi"
Everything is done within a watch command that refreshes the display every two seconds. Then, the panels are built up by a succession of commands separated with semicolons, one for each portion of the display. The grep command is used to pick out the desired output of the sensors command that is piped to it; doing that twice gets us two lines. The next command, echo "" | tee /dev/fd/2, adds an extra line by sending a space to STDERR output before the output of nvidia-smi is displayed. The result can be seen in the screenshot below.
However, I also came across a more graphical way to do things using commands like turbostat or sensors along with AWK programming and ttyplot. Using the temperature output from the above and converting that needs the following:
while true; do sensors | grep -i 'tctl' | awk '{ printf("%.2f\n", $2); fflush(); }'; sleep 2; done | ttyplot -s 100 -t "CPU Temperature (Tctl)" -u "°C"
This is done in an infinite while loop to keep things refreshing; the watch command does not work for piping output from the sensors command to both the awk and ttyplot commands in sequence and on a repeating, periodic basis. The awk command takes the second field from the input text, formats it to two places of decimals and prints it before flushing the output buffer afterwards. The ttyplot command then plots those numbers on the plot seen below in the screenshot with a y-axis scaled to a maximum of 100 (-s), units of °C (-u) and a title of CPU Temperature (Tctl) (-t).
A similar thing can be done for the CPU wattage, which is how I learned of the graphical display possibilities in the first place. The command follows:
sudo turbostat --Summary --quiet --show PkgWatt --interval 1 | sudo awk '{ printf("%.2f\n", $1); fflush(); }' | sudo ttyplot -s 200 -t "Turbostat - CPU Power (watts)" -u "watts"
Handily, the turbostat can be made to update every so often (every second in the command above), avoiding the need for any infinite while loop. Since only a summary is needed for the wattage, all other output can be suppressed, though everything needs to work using superuser privileges, unlike the sensors command earlier. Then, awk is used like before to process the wattage for plotting; the first field is what is being picked out here. After that, ttyplot displays the plot seen in the screenshot below with appropriate title, units and scaling. All works with output from one command acting as input to another using pipes.
All of this offers a lightweight way to keep an eye on system load, with the top command showing the impact of different processes if required. While there are graphical tools for some things, command line possibilities cannot be overlooked either.
Upheaval and miniaturisation
4th March 2025The ongoing AI boom got me refreshing my computer assets. One was a hefty upgrade to my main workstation, still powered by Linux. Along the way, I learned a few lessons:
- Processing with LLM's only works on a graphics card when everything can remain within its onboard memory. It is all too easy to revert to system memory and CPU usage, given the amount of memory you get on consumer graphics cards. That applies even with the latest and greatest from Nvidia, when the main use case is for gaming. Things become prohibitively expensive when you go on from there.
- Even with water cooling, keeping a top of the range CPU cool and its fans running quietly remains a challenge, more so than when I last went for a major upgrade. It takes time for things to settle down.
- My Iiyama monitor now feels flaky with input from the latest technology. This is enough to make me look for a replacement, and it is waking up from dormancy that is the real issue. While it was always slow, plugging out from mains electricity and then back in again is a hack that is needed all too often.
- KVM switches may need upgrading to work with the latest graphical input. The monitor may have been a culprit with the problems that I was getting, yet things were smoother once I replaced the unit that I had been using with another that is more modern.
- AMD Ryzen 9 chips now have onboard graphics, a boon when things are not proceeding too well with a dedicated graphics card. Even though this was not the case when the last major upgrade happened, there were no issues like what I faced this time around.
- Having LED's on a motherboard to tell what might be stopping system startup is invaluable. This helped in July 2021 and averted confusion this time around as well. While only four of them were on offer, knowing which of CPU, DRAM, GPU or system boot needs attention is a big help.
- Optical drives are not needed any longer. Booting off a USB drive was enough to get Linux Mint installed, once I got the image loaded on there properly. Rufus got used, and I needed to select the low-level writing option before things proceeded as I had hoped.
Just like 2021, the 2025 upgrade cycle needed a few weeks for everything to settle down. The previous cycle was more challenging, and this was not just because of an accompanying heatwave. The latest one was not so bedevilled.
Given the above, one might be tempted to go for a less arduous path, like my acquisition of an iMac last year for another place that I own. After all, a Mac Mini packs in quite a lot of power, and it is not the only miniature option. Now that I have one, I have moved image processing off the workstation and onto it. The images are stored on the Linux machine and edited on the Mac, which has plenty of memory and storage of its own. There is also an M4 chip, so processing power is not lacking either.
It could have been used for work affairs, yet I acquired a Geekom A8 for just that. Though seeking work as I write this, my being an incorporated freelancer means that having a dedicated machine that uses my main monitor has its advantages. Virtualisation can allow drift from business affairs to business matters, that is not so easy when a separate machine is involved. There is no shortage of power either with an AMD Ryzen 9 8945HS and Radeon 780M Graphics on board. Add in 32 GB of memory and 2 TB of storage and all is commodious. It can be surprising what a small package can do.
The Iiyama's travails also pop up with these smaller machines, less so on the Geekom than with the Mac. The latter needs the HDMI cable to be removed and reinserted after a delay to sort out things. Maybe that new monitor may not be such an off the wall idea after all.
Dropping back to a full screen terminal session from a desktop one in Linux
29th May 2014There are times when you might need to access a full screen terminal from a Linux graphical desktop. For example, I have needed this when installing Nvidia's graphics drivers on Ubuntu or Linux Mint. Another instance occurred on Arch Linux when a Cinnamon desktop update prevented me from opening a terminal window. The full screen command let me install an alternative terminal emulator, with Tech Drive-in's list proving helpful. Similar issues might need fixing on FreeBSD installations. These latter examples happened within VirtualBox, which has special requirements for accessing full screen command line sessions, which I'll explain later.
When running Linux on a physical PC, press CTRL + ALT + F1 to enter a full screen terminal and CTRL + ALT + F7 to return to the graphical desktop. In a Linux VirtualBox guest with a Linux host, these shortcuts affect the host instead. For the guest OS, use [Host Key] + F1 to enter a full screen terminal and [Host Key] + F7 to return to the graphical desktop. The default Host Key is the right CTRL key, unless you've changed it.
X sessions in GNOME and Cinnamon desktop environments support this functionality, but I can't confirm it works with alternatives like Wayland. Hopefully, this feature extends to other setups, as terminal sessions are occasionally needed for system recovery. Such mishaps are thankfully rare and should be virtually non-existent for most users.
A need to update graphics hardware
16th June 2013As someone who doesn't play computer games, I rarely prioritise graphics card upgrades. Yet, I recently upgraded graphics cards in two of my PCs despite nothing being broken. My backup machine, built nearly four years ago, has run multiple Linux distributions. It uses an ASRock K10N78 motherboard from MicroDirect with an integrated NVIDIA graphics chip that performs adequately, if not exceptionally. The only issue was slightly poor text rendering in web browsers, but this alone wasn't enough to justify adding a dedicated graphics card.
More recently, I ran into trouble with Sabayon 13.04 with only the 2D variant of the Cinnamon desktop environment working on it and things getting totally non-functional when a full re-installation of the GNOME edition was attempted. Everything went fine until I added the latest updates to the system, when a reboot revealed that it was impossible to boot into a desktop environment. Some will relish this as a challenge, but I need to admit that I am not one of those. In fact, I tried out two Arch-based distros on the same PC and got the same results following a system update on each. So, my explorations of Antergos and Manjaro have to continue in virtual machines instead.
When I tried Linux Mint 15 Cinnamon, it worked perfectly. However, newer distributions with systemd didn't work with my onboard NVIDIA graphics. Since systemd will likely come to Linux Mint eventually, I decided to add a dedicated graphics card. Based on good past experiences with Radeon, I chose an AMD Radeon HD 6450 from PC World, confirming it had Linux driver support. Installation was simple: power off, insert card, close case, power on. Later, I configured the BIOS to prioritise PCI Express graphics, though this step wasn't necessary. I then used Linux Mint's Additional Driver applet to install the proprietary driver and restarted. To improve web browser font rendering, I selected full RGBA hinting in the Fonts applet. The improvement was obvious, though still not as good as on my main machine. Overall, the upgrade improved performance and future-proofed my system.
After upgrading my standby machine, I examined my main PC. It has both onboard Radeon graphics and an added Radeon 4650 card. Ubuntu GNOME 12.10 and 13.04 weren't providing 3D support to VMware Player, which complained when virtual machines were configured for 3D. Installing the latest fglrx driver only made things worse, leaving me with just a command line instead of a graphical interface. The only fix was to run one of the following commands and reboot:
sudo apt-get remove fglrx
sudo apt-get remove fglrx-updates
Looking at the AMD website revealed that they no longer support 2000, 3000 or 4000 series Radeon cards with their latest Catalyst driver, the last version that did not install on my machine since it was built for version 3.4.x of the Linux kernel. A new graphics card then was in order if I wanted 3D graphics in VMware VM's and both GNOME and Cinnamon appear to need this capability. Another ASUS card, a Radeon HD 6670, duly was acquired and installed in a manner similar to the Radeon HD 6450 on the standby PC. Apart from not needing to alter the font rendering (there is a Font tab on the Gnome Tweak Tool where this can be set), the only real exception was to add the Jockey software to my main PC for installation of the proprietary Radeon driver. The following command does this:
sudo apt-get install jockey-kde
After completing installation, I ran the jockey-kde command and selected the first driver option. Upon restart, the system worked properly except for an AMD message in the bottom-right corner warning about unrecognised hardware. Since there were two identical entries in the Jockey list, I tried the second option. After restarting, the incompatibility message disappeared and everything functioned correctly. VMware even ran virtual machines with 3D support without any errors, confirming the upgrade had solved my problem.
Hearing of someone doing two PC graphics card upgrades during a single weekend may make you see them as an enthusiast, but my disinterest in computer gaming belies this. Maybe it highlights that Linux operating systems need 3D more than might be expected. The Cinnamon desktop environment now issues messages if it is operating in 2D mode with software 3D rendering and GNOME always had the tendency to fall back to classic mode, as it had been doing when Sabayon was installed on my standby PC. However, there remain cases where Linux can rejuvenate older hardware and I installed Lubuntu onto a machine with 10-year-old technology on there (an 1100 MHz Athlon CPU, 1GB of RAM and 60GB of hard drive space in a case dating from 1998) and it works surprisingly well too.
It appears that having fancier desktop environments like GNOME Shell and Cinnamon means having the hardware on which it could run. For a while, I have been tempted by the possibility of a new PC, since even my main machine is not far from four years old either. However, I also spied a CPU, motherboard and RAM bundle featuring an Intel Core i5-4670 CPU, 8GB of Corsair Vengeance Pro Blue memory and a Gigabyte Z87-HD3 ATX motherboard included as part of a pre-built bundle (with a heat sink and fan for the CPU) for around £420. Even for someone who has used AMD CPU's since 1998, that does look tempting, but I'll hold off before making any such upgrade decisions. Apart from exercising sensible spending restraint, waiting for Linux UEFI support to mature a little more may be no bad idea either.
Update 2013-06-23: The new graphics card in my main machine works well and has reduced system error messages; Ubuntu GNOME 13.04 likely had issues with my old card. On my standby machine, I found and removed a rogue .fonts.conf file in my home directory, which dramatically improved font display. If you find this file on your system, consider removing or renaming it to see if it helps. Alternatively, adjusting font rendering settings can improve display quality, even on older systems like Debian 6 with GNOME 2. I may test these improvements on Debian 7.1 in the future.
Changing from to Nvidia Graphics Drivers on Linux Mint Debian Edition 64-bit
22nd April 2012One way of doing this is to go to the Nvidia website and download the latest file from the relevant page on there. Then, the next stage is to restart your PC and choose rescue mode instead of the more usual graphical option. This drops you onto a command shell that is requesting your root password. Once this is done, you can move onto the next stage of the exercise. Migrate to the directory where the *.run file is located and issue a command similar to the following:
bash NVIDIA-Linux-x86_64-295.40.run
The above was the latest file available at the time of writing, which may have changed by the time that you read this. If the executable asks to modify your X configuration file, I believe that the best course is to let it do that. Editing it yourself or running nvidia-xconfig are alternative approaches if you so prefer.
Since proprietary Nvidia drivers are included in the repositories for Linux Mint Debian Edition, that may be a better course of action since you will get updates through normal system update channels. Then, the course of action is to start by issuing the following installation commands:
sudo apt-get install module-assistant
sudo apt-get install nvidia-kernel-common
sudo apt-get install nvidia-glx
sudo apt-get install kernel-source-NVIDIA
sudo apt-get install nvidia-xconfig
Once those have completed, issuing the following in turn will complete the job ahead of a reboot:
sudo m-a a-i nvidia
sudo modprobe nvidia
sudo nvidia-xconfig
If you reboot before running the above like I did, you will get a black screen with a flashing cursor instead of a full desktop because X failed to load. Then, the remedy is to reboot the machine and choose the rescue mode option, provide the root password and issue the three commands (at this point, the sudo prefix can be dropped because it's unneeded) then. Another reboot will see order restored and the new driver in place. Running the following at that point will do a check on things, as will be the general appearance of everything:
glxinfo | grep render