The Senior Analyst in Accenture's Business, Industry & Functional Architect track converts endeavor problems into platform-ready requirements. The function matters since unclear requirements can postpone endeavor transformation, addition rework and weaken person adoption. Strong candidates display they can comprehend processes, ask exact questions and nexus endeavor outcomes alongside innovation delivery.

This guide is built for evidence candidates preparing for Accenture's Business Requirement Analysis role. It covers the function context, required skills, discussion process, role-specific questions, preparedness topics, a four-week scheme and growth path. Candidates aiming for a broader progression can additionally difference this function alongside the Accenture Team Lead Business, Industry & Functional Architect discussion guide.

1. About the Senior Analyst - Business, Industry & Functional Architect ( Business Requirement Analysis ) Role

This function owns the extend between endeavor users and innovation teams. A Senior Analyst studies how activity happens today, identifies anywhere delays or manual attempt exist, and converts the improved procedure into functional requirements, workflows, reports, admission rules and evaluation scenarios that shipment teams can build against.

Day to day, the function plant alongside client endeavor users, topic matter experts, resolution architects, developers, testers, alter teams and project managers. The activity sits inner Accenture Technology and supports endeavor transformation programs anywhere platforms, data, synthetic intellect tools and procedure redesign arrive together.

2. Required Skills and Qualifications

Role Qualifications

Skills Overview

3. Day-to-Day Responsibilities

A representative week combines workshops, documentation, data review, evaluation assistance and follow-ups alongside shipment teams. The Senior Analyst is expected to keep requirements clear, decisions recorded and endeavor users aligned as the resolution moves from idea to release.

4. Key Competencies for Success

High performers in this function merge consulting site alongside applicable shipment sense. They do not halt at collecting requirements. They explain ambiguity, acknowledge endeavor impact, difficulty feeble assumptions and communicate trade-offs without losing stakeholder trust.

5. Interview Process at Accenture

For evidence and expert hiring, candidates are normally assessed throughout aptitude, communication, functional understanding, problem-solving and civilization fit. For this role, anticipate additional accent on endeavor scenarios, necessity clarity, stakeholder handling, Excel or SQL basics and your capability to explain projects in a consulting-friendly structure.

6. Interview Questions

Use Situation, Task, Action, Result for behavioural answers, and use a apparent step-by-step construction for functional scenarios. Accenture interviewers normally value clarity, client orientation, applicable trade-offs and evidence that you can activity between endeavor and innovation teams.

7. Topics and Areas of Focus

Preparation should concentration on applicable endeavor inspection skills, not lone definitions. You have to be capable to explain how a necessity moves from stakeholder conversation to procedure design, functional specification, testing, reporting and adoption.

8. Preparation Plan

Pre-Interview Checklist

9. Career Growth & Next Roles

Growth from this function depends on shipment reliability, client trust, functional degree and capability to own larger resolution areas. Candidates who move beyond project implementation into procedure ownership, resolution recommendations and squad direction are improved positioned for guide responsibilities.

For candidates preparedness a guidance path, the Accenture Team Lead Business, Industry & Functional Architect guide shows how expectations alter whenever you commencement owning people, shipment governance and larger workstreams.

10. Conclusion

The most crucial preparedness move is to practise turning messy endeavor situations into apparent requirements, procedure maps, test cases and measurable outcomes. Accenture volition value candidates who can display organized thinking, not candidates who lone recall definitions of endeavor inspection terms.

Before the interview, prepared one powerful project narrative and one end-to-end endeavor procedure case. Be prepared to explain how you would collect requirements, validate data, assistance testing, oversee admission roles and track acceptance following go-live.

Top Tips for Interview Success

The Senior Analyst in Business, Industry and Functional Architecture for Supply Chain Management and Planning helps change messy functioning problems into usable phase designs. The function matters since preparedness errors can power inventory, assistance levels, manufacturing schedules, fulfilment promises and administrator decisions.

This guide is for evidence candidates and early-career professionals preparing for Accenture interviews in endeavor transformation, provision sequence preparedness and endeavor phase roles. It covers the role, skills, discussion process, role-specific questions, case custom and a focused preparedness plan.

1. About the Senior Analyst - Business, Industry & Functional Architect (Supply Chain Management & Planning ) Role

This function translates endeavor preparedness needs into functional designs that shipment teams can build and test. You may map how petition forecasts move into provision plans, anywhere endorsement controls are needed, which reports leaders need, and how users should activity following a phase change.

You activity between client endeavor users, topic matter experts, developers, testers and project managers. If you desire a broader perspective of the identical job family beyond provision sequence planning, difference this guide alongside the Accenture Senior Analyst Business, Industry and Functional Architect discussion guide.

2. Required Skills and Qualifications

Confirmed Qualifications

Skills Overview

3. Day-to-Day Responsibilities

A representative week combines client discussions, procedure analysis, data checks, scheme documentation, evaluation assistance and shipment coordination. The activity is practical: you change ambiguous provision sequence issues into organized decisions that a phase squad can build, validate and deploy.

4. Key Competencies for Success

High performers merge consulting construction alongside operational detail. They can explain a provision sequence issue in endeavor language, rotate it into phase requirements, and keep distinct teams aligned whenever trade-offs appear during design, evaluation or go-live.

5. Interview Process at Accenture

For evidence and early-career consulting innovation roles, prepared for an assessment-led procedure followed by communication, technical-functional and managerial evaluation. Expect interviewers to test how you think through endeavor problems, how you use data, and whether you can activity in a client-facing shipment environment.

6. Interview Questions

Use STAR for behavioural answers, MECE for case answers, and a step-by-step walkthrough for specialized questions. Accenture interviewers normally value clarity, applicable shipment thinking and the capability to nexus endeavor goals alongside innovation execution.

7. Topics and Areas of Focus

Preparation should concentration on the skills that appear in genuine shipment conversations: provision sequence trade-offs, endeavor analysis, procedure design, data validation, evaluation and client communication. Do not prepared this akin a coding role; prepared it akin a technology-enabled consulting role.

8. Preparation Plan

Pre-Interview Checklist

9. Career Growth & Next Roles

Progression depends on shipment ownership, client trust, domain degree and the capability to guide younger squad members. A Senior Analyst who can own requirements, power scheme decisions and assistance gentle go-lives can move toward consultant, squad guide or expert provision sequence transformation tracks.

Candidates thinking onward to guidance expectations can additionally assessment the Accenture Team Lead Business, Industry and Functional Architect discussion guide to comprehend how evaluation changes at the next level.

10. Conclusion

The strongest candidates prepared for this discussion as a endeavor innovation role, not as a generic expert role. Your answers should display how you diagnose provision sequence problems, scheme platform-ready requirements, validate data and assistance users through evaluation and adoption.

Before the interview, build one complete example that covers necessity gathering, procedure mapping, dashboard thinking, User Acceptance Testing and alter impact. Practise explaining it in uncomplicated language, since this function rewards candidates who can create complex shipment activity understandable to the two endeavor and specialized audiences.

Top Tips for Interview Success

Ernst & Young ( EY): Tax Analyst Interview Guide

A Tax Analyst at Ernst & Young ( EY) supports earth tax compliance activity that helps client teams document exact returns, validate financial data, oversee reporting deadlines and decrease compliance risk. The function sits in EY Global Delivery Services, anywhere analysts activity alongside cross-border tax teams on corporate, partnership, individual, payroll, indirect tax and mobility-related assignments.

This guide is built for B.Com, BBA, B.Sc, M.Com and MBA Finance students preparing for evidence hiring or early-career Tax Analyst interviews. Use it alongside Board Infinity's EY discussion preparedness guide to comprehend the wider choice style, afterward concentration current on tax, accounting, Excel and case-based preparation.

1. About the Tax Analyst Role

The function converts client data into compliance-ready workpapers, tax come back inputs, reconciliations and assessment notes. A fresher is not expected to create final tax positions independently, but is expected to inspect data carefully, emblem mismatches early, document assumptions and complete assigned activity inside service-level agreements (SLAs).

Day-to-day activity normally involves elder tax professionals, reviewers, earth client-serving teams, payroll or backing contacts and another Global Delivery Services (GDS) colleagues. EY associate firms activity throughout assurance, consulting, tax, scheme and transactions services, as described on the authoritative EY services page.

2. Required Skills and Qualifications

Educational Qualifications

Skills Overview

3. Day-to-Day Responsibilities

A representative week combines tax come back preparation, data validation, documentation, follow-ups and assessment comment resolution. Work can change throughout endeavor tax, idiosyncratic tax, indirect tax, payroll assistance or movement compliance depending on involvement needs.

4. Key Competencies for Success

High performers in this function merge careful implementation alongside expert judgement. EY interviews frequently reward candidates who can explain how they study fast, defend quality, communicate throughout teams and remain calm during deadline-heavy compliance cycles.

5. Interview Process at Ernst & Young ( EY)

For evidence and fresher hiring, anticipate eligibility screening followed by accomplishment checks and competency-based interviews. Prepare for accounting basics, tax awareness, Excel, communication, ownership stories and willingness to activity in earth assistance shipment environments.

6. Interview Questions

Use STAR for behavioral answers, a step-by-step calculation method for specialized questions and a MECE construction for case prompts. EY interviewers prioritise clarity, norm mindset, confidentiality, willingness to study and the capability to communicate exceptions without sounding defensive.

7. Topics and Areas of Focus

Preparation have to be practical, not lone theoretical. Master the areas that immediately power come back preparation, workpaper quality, reviewer confidence and your capability to run in a earth tax shipment environment.

8. Preparation Plan

Pre-Interview Checklist

9. Career Growth & Next Roles

Progression from Tax Analyst depends on accuracy, reviewer trust, speed of learning, communication and capability to grip additional complex records alongside small supervision. Over time, analysts can move toward elder preparation, assessment responsibilities, client coordination, tax technology, earth mobility, indirect tax or endeavor tax specialisation.

10. Conclusion

The strongest preparedness is to nexus all answer to quality, records and deadline discipline. EY is not lone evaluation whether you recall tax definitions, but whether you can use them carefully to financial data, payroll records, indirect tax records and earth compliance workflows.

Before the interview, build one reconciliation file, practise specialized answers aloud and prepared STAR stories that display ownership under pressure. If you desire an additional tax-role comparison, assessment Board Infinity's Grant Thornton Tax Analyst discussion guide and notice how tax fundamentals remain crucial throughout firms.

Top Tips for Interview Success

LIXIL is a Japanese manufacturer of water, kitchen, accommodation and construction products used in homes, business spaces and community infrastructure. Its portfolio includes toilets, faucets, showers, washbasins, bathtubs, kitchens, windows, doors, exterior products and façade-related solutions, sold through brands specified as INAX, GROHE, American Standard and TOSTEM. The business sits at the intersection of manufacturing, design, construction, provision chain, sustainability and person experience, so it is applicable to candidates throughout engineering, product, operations, procurement, sales, marketing, backing and company roles. LIXIL matters in interviews since its activity is not a clean consumer-goods narrative or a clean building story; it combines durable merchandise design, conduit partnerships, facility realities, brand positioning and lengthy merchandise lifecycles. A applicant who understands that mix can explain endeavor decisions alongside additional environment than person who lone studies the business as a brand name.

This guide covers anywhere LIXIL came from, who runs and owns it today, and the merchandise areas in which it competes.

1. Company Overview

About LIXIL

LIXIL describes itself as a creator of pioneering h2o and accommodation products, alongside a portfolio that spans sanitary ware, fittings, kitchens, windows, doors, exterior products and building-related solutions (LIXIL company overview). That makes it a helpful discussion case study since the endeavor connects manufacturing manufacturing alongside person expectations, expert installer networks, architecture, sustainability standards and after-sales service.

Company History

The key discussion takeaway is that LIXIL is a contemporary company phase built from multiple established Japanese merchandise businesses fairly than a single-brand manufacturer. Its afterward enlargement into global washroom and sanitary brands additionally method candidates have to be prepared to conversation integration, brand portfolio administration and functioning site throughout distinct markets.

2. Mission, Vision & Values

Mission

LIXIL presents this as its authoritative Purpose, not as a separately labelled goal statement, on its Purpose page; use it in interviews to nexus merchandise activity alongside mundane living conditions.

Vision

LIXIL puts its authoritative Purpose and LIXIL Behaviors at the centre of its community civilization messaging fairly than foremost alongside a distinct imagination line.

Core Values

LIXIL publishes its values for workforce as LIXIL Behaviors, using the following wording on its authoritative Purpose and civilization material.

Prepare discussion stories against this exact list: one example showing ethical judgment, one showing collaboration throughout differences, and one showing learning through a test or betterment cycle.

3. Products and Services

LIXIL's offering is built about the parts of a construction that group contact all day: bathrooms, kitchens, openings, exteriors and façade-related systems. The business groups its portfolio about h2o and accommodation products, so it is particularly applicable for candidates in manufacturing, merchandise design, quality, sourcing, sales, conduit management, project endeavor and sustainability-linked roles.

• Bathroom and sanitary ware: LIXIL covers toilets, ablution toilets, washbasins, bathtubs and washroom systems through brands specified as INAX and American Standard, making this area applicable for candidates who comprehend hygiene, ergonomics, ceramics, facility and assistance quality.
• Faucets, showers and h2o fittings: The business sells taps, mixers, ablution systems and connected water-control products, including premium fittings connected alongside GROHE and design-led washroom solutions anywhere reliability, h2o use and person cognition matter.
• Kitchen products: LIXIL's kitchen-related range includes scheme kitchens, sinks, retention units and fittings, so candidates can conversation how design, durability, modularity and after-sales coordination power a high-involvement residence purchase.
• Windows, doors and openings: Its accommodation innovation portfolio includes windows, entrance doors, inner doors and connected beginning systems, anywhere achievement depends on materials, climate resistance, insulation, safety and compatibility alongside local building practices.
• Exterior and outdoor products: LIXIL additionally operates in products specified as gates, fences, carports, terraces and garden-room manner exterior solutions, which nexus the business to residential planning, curb appeal, climate safety and installer-led selling.
• Building and façade-related solutions: For larger projects, LIXIL's construction merchandise capability extends into architectural openings and exterior-facing systems, making project coordination, specification selling, compliance and contractor relationships crucial for endeavor roles.

4. Key Competitors of LIXIL

LIXIL competes in building-materials and water-technology categories anywhere merchandise quality, scheme range, allocation depth, after-sales service, cost architecture and project-specification relationships all matter. Its difference set changes by category: a recruiter may anticipate you to differentiate between earth premium bathing brands, India-focused sanitaryware players, and companies alongside powerful contractor or retailing networks.

In an interview, construction a competitive-analysis answer by comparing LIXIL and its rivals on category overlap, mark segment, allocation model, scheme differentiation, assistance capability and cost tier fairly than treating business size as the entire argument.

5. Corporate Social Responsibility (CSR)

LIXIL’s CSR lens is closely tied to the products it makes: toilets, faucets, bathing systems, kitchens, windows and construction materials power hygiene, h2o use, accessibility and mundane norm of life. The business describes sustainability through areas specified as sanitation and hygiene, h2o conservation and ecological sustainability, and inclusion on its official sustainability pages.

Before naming a particular undertaking in an interview, verify the latest LIXIL sustainability report, local nation leaf or latest business communication so your example is current.

6. Interview Process & Selection Rounds

LIXIL hiring is normally role-led: a revenue opening, factory role, company function or merchandise function volition be assessed by the hiring squad closest to that work. The construction can change by country, seniority and function, but candidates should anticipate screening for endeavor fit, values alignment, functional capability and the capability to activity throughout brands, channels and stakeholders.

For early-career roles, the procedure may concentration additional on learning agility and communication; for informed roles, anticipate deeper conversation of endeavor outcomes, stakeholder handling and decision quality.

7. Common Interview Questions Across Roles

These are the questions any LIXIL applicant can visage any the function; role-specific specialized questions pertain in that role’s own guide.

8. Required Skills & Tools

Required skills at LIXIL are not one worldwide list; they alter by occupation route, and this array maps to the identical routes used in Career Opportunities below.

9. Career Opportunities at LIXIL

LIXIL careers are organised small akin one sole ladder and additional akin functional routes: business teams develop the market, merchandise and operations teams build and provision solutions, and enabling teams keep the endeavor scalable. For discussion preparation, map your cognition to the path closest to your day-to-day work, afterward display the measurable indication that proves you are prepared for the next level.

10. Future Outlook and Strategic Plan

LIXIL positions itself as a earth h2o and accommodation products business shaped by Japanese building-material roots, the 2011 innovation of LIXIL, and afterward brand enlargement including American Standard and GROHE, as recorded in its authoritative business history. For candidates, the clearest direction to comprehend is how the business balances bequest brands, merchandise innovation, liable h2o use, and local market execution.

11. Conclusion

LIXIL sits in a rivalrous building-products market anywhere rely is built through scheme credibility, dependable installation, assistance assistance and brand clarity. Its direction depends on how fine it connects Japanese merchandise heritage, earth brands and local client needs throughout homes, business spaces and sanitation use cases. For person joining, the chance is to activity on products that are apparent in regular existence during handling applicable constraints about cost, quality, channels and sustainability.

An Area Sales Manager at LIXIL owns district growth through dealers, sub-dealers, architects, builders, inner designers and plumbing influencers. The function matters since LIXIL sells bathroom, kitchen, accommodation and construction innovation products anywhere trust, merchandise display, advice and timely provision immediately power conversion.

This guide is built for MBA, BBA and early-career revenue candidates preparing for LIXIL interviews. It covers the role, required skills, regular work, competencies, apt discussion rounds, role-specific questions, case preparation, a 4-week scheme and a applicable final checklist.

1. About the Area Sales Manager Role

This function is liable for construction income in a defined district by strengthening the retailing and allocation channel. You volition acknowledge gaps in dealer coverage, designate dependable sub-dealers, create petition through specifiers and justify that orders, merchandise preparedness and collections remain on track.

LIXIL operates in h2o and accommodation products, alongside authoritative earth brands including INAX, GROHE, American Standard and TOSTEM on its LIXIL brands page. In regular work, the Area Sales Manager connects external conduit partners alongside inner sales, marketing, provision and backing teams.

2. Required Skills and Qualifications

Skills Overview

3. Day-to-Day Responsibilities

A representative week combines market visits, dealer reviews, influencer meetings, inner coordination and gathering follow-ups. The finest candidates display that they can oversee the two petition generation and implementation discipline, since the two power district performance.

4. Key Competencies for Success

High performers in this function merge market energy alongside implementation maturity. LIXIL sells design-led and installation-sensitive categories, so interviewers volition value candidates who can power multiple stakeholders without losing authority of orders, displays and collections.

5. Interview Process at LIXIL

For an Area Sales Manager function in a construction products company, anticipate a sales-led procedure that tests district thinking, conduit maturity, client power and fee discipline. The strongest preparedness is to practise abbreviated endeavor cases about dealer appointment, low secondary sales, competitor discounting and decorator conversion.

6. Interview Questions

Use STAR for behavioural answers, which method Situation, Task, Action and Result. For function cases, use a organized revenue framework: diagnose the territory, section stakeholders, quantify the gap, act through conduit and influencer levers, afterward track collections and execution.

7. Topics and Areas of Focus

Prepare for a revenue discussion that is practical, market-facing and numbers-aware. The topics below map immediately to the situations an Area Sales Manager faces during expanding dealer business, creating influencer petition and protecting collections.

8. Preparation Plan

Pre-Interview Checklist

9. Career Growth & Next Roles

Growth from an Area Sales Manager function normally depends on accordant mark delivery, norm of dealer expansion, gathering site and capability to power architects, builders and commerce partners. Candidates who build repeatable district systems are improved placed to move into larger geographies or local leadership.

10. Conclusion

The strongest way to prepared for the LIXIL Area Sales Manager discussion is to think akin a district owner. Every answer should nexus market coverage, dealer behaviour, specifier influence, retailing display, command fulfilment and collections alternatively of treating revenue as lone association building.

Prepare a 30-60-90 day scheme and practise cases on dealer appointment, low conversion, delayed provision and overdue payments. For additional area revenue comparison, assessment Board Infinity's Ather Energy Area Sales Manager discussion preparedness guide, particularly to difference conduit preparedness throughout distinct merchandise categories.

Top Tips for Interview Success

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MXC is a sandboxed code implementation system for operating untrusted code (model output, plugins, and tools) on Windows, Linux, and macOS. It provides multiple containment backends, from OS-native procedure sandboxes to complete VMs, behind a unified containment example and typed SDKs.

• Cross-platform: Windows, Linux, and macOS assistance with platform-appropriate containment backends
• JSON-based configuration: Versioned container-creation requests and security policies
• Multiple containment backends: ProcessContainer, Windows Sandbox, LXC, Bubblewrap, Seatbelt, MicroVM (Nanvix), Hyperlight, IsolationSession, and WSLC
• Policy-driven sandboxing:
  • Filesystem policy: Read-only, read-write, and denied way lists
  • Network policy: Proxy support, outbound controls, and backend-dependent host filtering
  • UI policy: Clipboard, display, and GUI admission controls
• State-aware lifecycle: Provision, start, execute, stop, and deprovision persistent containers
• Rust, .NET, and Node SDKs: Versioned APIs for one-shot and state-aware execution
• Diagnostics: Tools to comprehend access-denied failures in a container

MXC is an SDK dependency that builds into your app.

flowchart LR App["Your application<br/>Launch API"] --> SDK["MXC SDK<br/>Rust / .NET / Node<br/>(in process)"] SDK --> Backend["Selected backend<br/>(in process)"] Backend --> Container["Isolated workload<br/>ProcessContainer / WSLC / Bubblewrap / ..."] 

Your use specifies:

• The receptacle type
• The containment rules
• The workload command

MXC validates the request, selects the backend, and launches the workload in the resulting container.

MXC runs workloads through platform-appropriate receptacle backends on Windows, Linux, and macOS.

* These backends are experimental.

Install an SDK through your bundle manager. You do not need to copy this repository.

The Node and .NET packages contain the native runtime assets. The Rust crate builds the MXC SDK, engine, and selected backends into the consuming application.

Non-SDK consumption: Platform-specific administrator binaries, specified as wxc-exec.exe, obtain JSON container-creation requests defined by the stable schema. Use for evaluation or whenever the SDK cannot be embedded in your app.

For complete SDK samples, see the Rust, .NET, and Node samples.

import { spawn, type ContainerRequest } from '@microsoft/mxc-sdk/v1'; const request: ContainerRequest = { command: 'node -e "console.log(\'hello from container\')"', network: { egress: { default: 'deny' } }, timeoutMs: 30_000, }; const child = await spawn(request);

See the runnable streaming standard-I/O sample and the SDK API reference.

Your use volition hit admission issues whenever operating in a sandbox, until you've had period to tune your containment rules. We're current to help.

Native executors normally reserve norm input, output, and error for the workload. Use --debug for MXC diagnostic output:

wxc-exec.exe --debug config.json

See MXC diagnostics for the complete developer reference.

Warning: --audit turns off all sandbox safety for the workload being analyzed. Never use it to run untrusted code.

Audit manner helps a guideline author discover access-denied failures and rebuild a ProcessContainer guideline that grants the records and capabilities a trusted tool actually needs. On supported Windows releases, run:

wxc-exec.exe --audit policy.json

MXC records the observed accesses and produces policy-authoring artifacts. See logging admission denied for safe deny-and-record diagnostics, audit outputs, and supported workflows.

Official Microsoft builds can dispatch optional diagnostic telemetry to Microsoft. Telemetry is off unless the idiosyncratic run opts in, the Windows person has explicitly consented, administrative guideline permits collection, and your application enables the telemetry choice in a contained workload request. An administrator can obstacle telemetry but cannot aid consent for the user.

Local open-source builds are not configured to path telemetry to Microsoft, and telemetry is a no-op on non-Windows platforms. See telemetry guideline and consent for controls and privacy details.

Build from origin whenever evolving MXC, changing the native runtime, or using the standalone administrator binaries alternatively of a packaged SDK. Repository builds produce the platform-native runtime and executors and phase the native assets used by the Node SDK.

Build prerequisites are:

• Rust, pinned to type 1.93 by src/rust-toolchain.toml
• Node.js 24 or afterward and npm
• The phase toolchain and prerequisites described by the selected backend

build.bat --all # Release build for current architecture

./build.sh --all # Release build

./build-mac.sh --all # Release build for native architecture

Repository contributors should commencement alongside the MXC betterment documentation.

See CONTRIBUTING.md for contribution guidelines.

See LICENSE.md for details.

click (or media Enter) to commencement  ·  afterward media any key for the menu

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[This is a visitant article by Álvaro Lozano-Robledo. This blog article was initially written in a distinct document format and converted using AI. — T.]

TL;DR: Keep calm and transport on studying math.

I would akin to provision Terry my heartfelt gratitude for giving me the chance to contribute a article to his blog. After giving much idea to what topic I should compose concerning to maximize impact, I decided to obtain this chance to attain out to the students: particularly to those undergraduate and alumnus students who fair a few months ago were dreaming of an scholarly occupation in mathematics, but their dreams may now appear distant and, for some, apparently unattainable to always rotate into a reality. This article was inspired by a communication (quoted below in its entirety, alongside permission) that I received from a pupil desperately looking for direction and guidance. This is not the lone specified communication I have received (and I doubtful that many of us are receiving many akin requests), but it is perchance the most heartfelt, and the one that has moved me the most. Please additionally note the urgency of the message. Students are making decisions now.

Hey Prof, I’ve been observing your videos for a during now as a clean math undergraduate who formerly wanted to prosecute a occupation in math academia. I cognize you likely have been getting a lot of questions concerning this matter, but I am fair entirely at an utter defeat concerning my occupation trajectory, and equal further, the definition of existence at this point. (I do acknowledge a lot of group have it much worse than I do). I cognize you have been making a lot of videos lately alongside the new LLM advancement updates, so I idea you power be the suitable individual to attain out to and get a slightly additional organized answer concerning this matter. So, to cut to the chase, what I really desire to cognize is: volition math academia be big adequate and accessible adequate for anyone alongside sheer enthusiasm (despite not being the brightest intellect in the field) to prosecute a occupation in, or volition it inevitably shrink specified that it volition lone really be accessible to the brightest minds? (I do acknowledge the “brightest minds” that I am mentioning current is not well-defined, and in a sense, I am taking it as a assumption that this is person who is “smarter than me”). My second inquiry is, volition AI inside 5–10 years surpass humans in being capable to do clean math research? I’ve fair really been misplaced for a brace of months now and misplaced in existence completely. I don’t average to create your day additional depressing; apologetic if I arrive off in any way of that sort. I would value any advice.

The advances in LLMs are disrupting nearly all aspects of scholarly investigation and learning in math and, during there are many aspects that involvement me, the one sole matter that worries me the most is the extremely genuine possible that we are concerning to endure an complete generation of mathematicians. Many students are asking themselves whether going for a PhD in math is the correct occupation move at this time. Many of them fair a twelvemonth ago were headed to grad academy in mathematics, but they are now changing their mind, and think that a distinct occupation (as far from math as Law School) may be the finest way stated the danger that AI may entirely alter the scholarly math landscape in the coming months.

The questions students are worrying concerning are as follows:

1. Will AI surpass the mathematical investigation capability of any human?
2. Will investigation mathematicians rotate into `professional prompters’ and interpreters of LLM output?
3. Will lone the `brightest minds’ be capable to meaningfully contribute to investigation mathematics?
4. Will mathematicians be employable? Will mathematicians be needed?
5. Should I prosecute a PhD in math at this time?

In this composition I volition try to location these questions to the finest of my ability, but volition commencement alongside two disclaimers, followed by a concise summary of my own outlook.

Disclaimer 1. My answers may “age akin milk,” as YouTube commenters affection to quip on older videos. I can live alongside that, since this article expresses how I and many of us in the community about me awareness today. Things can alter quickly, although (see Disclaimer 2). I additionally desire to acknowledge my privileged item of perspective as a tenured prof in math — the circumstance can appearance much additional troubling from the item of perspective of the job insecurity of a extremely early-career mathematician.

Disclaimer 2. No one has the answers at this time. I desire to create apparent from the commencement that no one can cognize alongside certainty the answers to any of the questions posed above: not any particular Fields medalist, not any stated mathematician, not any particularly vociferous AI expert, and not the frontier example companies. And if person is telling you alongside bonzer confidence what the forthcoming holds, afterward I would immediately distrust the motives of their condemnation (anecdotically, nearly anyone on X.com that predicts the triumph of AI and the demise of the math profession, is either a self-proclaimed “AI expert” or plant for an AI startup). No one has a apparent image since betterment of LLMs has been so accelerated (and opaque) that it is nearly unattainable to foretell what is to come. A fine part of direction is to ask the identical questions to many people, to comprehend a (hopefully balanced) range of opinions. To that end, I am collecting interviews alongside mathematicians in what I call the “Human Mathematicians in the Age of AI” video project. I advance you to hear to the interviews for several awesome points.

For the record: I do not have the answers either, but I am hopeful and enthusiastic for the future. I volition explain why below.

Who is controlling the narrative concerning LLMs in math? Overall, the mathematical community’s reactions to the advances in AI have ranged from disturbance to rage — but, mostly, disturbance concerning how to proceed. The most dystopian predictions appear to be driven by the fact that the so-called frontier example companies (and another LLM-powered companies) are controlling the narrative in the finest of their interests. Unfortunately, the finest company outcomes for an LLM business could have possible catastrophic outcomes for the math (and scientific) community.

It is certain that AI companies desire us to accept that their products volition imminently accomplish “super-human intelligence” and that, in particular, they volition be capable to autonomously resolve any mathematical issue a individual could resolve alongside or without the aid of an LLM. It is in their finest company involvement that the community is convinced of the (allegedly) “unlimited potential” of their technology, particularly before their companies’ stocks go community (i.e., their upcoming IPOs: Anthropic in November 2026, OpenAI in first 2027, etc). Thus, they have tried to authority the narrative by spending a huge amount of (human and computational) resources in command to discover solutions to certain well-known mathematical problems. The proofs are afterward released in announcements that guide the community to accept that their models can already autonomously resolve any issue at all, and swiftly at that. However, this is (currently) far from their true capabilities. For instance, they never conversation how many tokens have gone to the trash bin alongside no payoff in trying (and failing) to resolve celebrated problems. We do know, for example, that OpenAI invested the equal of several $15M to resolve (err, scoop) the Navier-Stokes problem, but we are unaware of the certainly colossal operating disbursal of the failures to determine another Millennium Prize problems.

My own outlook. Even although I am concerned concerning the incursions of LLMs into scholarly mathematics, I am fairly hopeful. In fact, I regard this to be the most breathtaking period in my mathematical occupation (since the twelvemonth 2000 say). Truly, this may be the most thrilling instant in math in the contemporary former of our discipline, and I would be terribly sad to see young group depart academia and young female out on the stunning chance to be at the frontlines of the current specialized revolution. And not fair sad: I think their deficiency would have disastrous effects for the field.

Undoubtedly, LLMs are already an incredibly mighty tool. If used correctly, and if we set up sensible scholarly behavior expectations about the use of LLMs, these tools can accelerate advancement in our site dissimilar in any former era. I completely anticipate that we, the community, volition modify and modify to this new period, and we volition harness these tools to accomplish really awesome things that fair a few months ago seemed far out of reach. And I completely anticipate that individual mathematicians volition be forefront and center in these fantastic achievements to come. I volition add reasons that assistance my optimism below.

I additionally desire to add at this item that the day-to-day of a mathematician has not changed much so far! My days are motionless filled alongside instruction and joyful conversations concerning math alongside colleagues and students, doing investigation on a figure of breathtaking (old and new) projects, and going to stimulating conferences to study and disseminate our most latest methods and findings, during spending period alongside colleagues that create the mathematical community so fantastic and vibrant. Daniel Litt mentioned the identical opinion in a latest tweet.

One item has changed though, I am busier than always before, since the figure of investigation projects I am engaged in has tripled in fair a few months. My investigation horizon has expanded significantly, and I have many additional projects accessible for students to assistance me with.

Now, to the pressing questions:

“Will AI surpass the mathematical investigation capability of any human?” This is entirely unclear. On one hand, the current trajectory in capabilities is certainly significant, and we have already seen many notable results that have been either proved by LLMs, or their proofs have been made imaginable gratitude to significant LLM contributions. On the another hand, none of the proofs so far appear to merge “alien ideas,” a move-37, or entirely novel arguments or new concepts that were not current in the writings in several form or another. This should not be shocking since the LLMs are built and trained on the entirety of all individual contributions to date, so it stands to logic that they would `think’ inside the boundaries of our current cognition and create connections (sometimes amazing and ingenious!) among ideas that are already current in the literature. I am particularly eager the assumption (or toy model, as he called it) put onward by Nestor Guillen in a latest blog post, anywhere he argues that LLMs may activity inside the confines of the convex hull of ideas that are currently accessible in the literature.

Take, for example, the disproof of Erdos’ unit-distance conjecture. We can ideate the current set of mathematical ideas as a stellated high-dimensional polytope, and we can location the state-of-the-art ideas on discrete geometry at an external vertex and our cognition on algebraic figure theory at a distinct external vertex. The idea for the evidence seems ingenious at archetypal perspective since it cleverly mixes strategies from two sectors of math at the vertices of the polytope of ideas, but following nearer inspection, it’s a evidence that was inside attain of humans as it fair sits inside the convex hull of the polytope.

The polytope of ideas

This agrees alongside what Melanie Matchett-Wood said concerning the evidence of the unit-distance whenever it was released: “I accept if the flat and category of individual ability that is represented on this note had been assembled to discover a counterexample to this conjecture a duration ago, and those group put in akin amounts of period operating on it than they did to study and thinking concerning Chat GPT’s solution, the mathematicians would have established a counterexample.”

However, a evidence of the Riemann hypothesis, say, may need new ideas that are strictly exterior of the convex hull of current mathematical ideas, and it is hence out of attain for an LLM. Only following a new idea is introduced in a new paper, the polytope of ideas may get a new external vertex. And lone afterward the LLMs, following being retrained to contain those ideas, may inhabit out the set of results up to the new convex hull, which may or may not contain yet a complete evidence of Riemann.

The convex hull of ideas

If this toy example holds up, afterward we would certainly anticipate the extremely accelerated advances in math that we are currently seeing. As the LLMs obtain advantage of the stellated nature of the polytope of ideas, they volition continue to inhabit in gaps between external spikes. But as the LLMs inhabit in the convex hull alongside new results, we volition see a deceleration in the figure of results being shown solely by synthetic intelligence. We volition need individual advances and intuition to create new ideas that develop our cognition polytope.

Even if the mathematical capability of the LLMs (or forthcoming AI models) can at several item attain beyond the convex hull of the current set of individual ideas, there is a distinct way that we may attain a bounds to the LLM capacity: feasibility and ethical use of resources (this is akin what chap optimist Kevin Buzzard called the “natural boundary” in a latest blog post). Is any disbursal (a dollar amount, individual cost, ethical cost) satisfactory in the chase of solving a stated problem? Should we expend millions of dollars and an undisclosed amount of natural resources in command to discover a resolution for Navier-Stokes? As an analogy: we would akin to cognize if there is existence on Mars, but in command to do so as shortly as possible, we would need an ridiculous amount of backing and hazard the lives of a individual squad in the process. Is it value it? Similarly, we may attain a item anywhere an LLM could resolve an crucial issue for an exorbitant disbursal (in conditions of backing and resources) but it may fair not be an satisfactory disbursal for the payer or community to bear. Instead, we volition need humans to devise an substitute path (the equal of a gravity-assisted robotic goal to Mars) to resolve the issue at an satisfactory cost, that produces a akin outcome in conditions of mathematical advances and, additional importantly, individual understanding.

“Will investigation mathematicians rotate into `professional prompters’ and interpreters of LLM output?” There is no sign that this volition be the case. Yes, LLMs have produced proofs of crucial results slightly autonomously (according to the frontier example companies — see Disclaimer 2) that several mathematicians have been tasked alongside interpreting and digesting. But in my own experience, and another investigation mathematicians who are using LLMs in their investigation appear to agree, operating alongside an LLM is akin to discussing a issue alongside a collaborator, and the results heavily depend on how much direction and intuition the mathematician inputs into the conversation. In another words, the LLMs are additional than tools: they can be investigation collaborators but, as in any collaboration, the cognition and the results are greatly improved whenever all parties contribute to the discussion. Further, mathematicians have no desire to immediate “solve the Riemann hypothesis, create no mistakes” and afterward construe the proof. We favor to be energetic participants during all the steps in the procedure of the finding of a proof, since we are motivated by the `why the outcome is true,’ additional than by the final answer that `the declaration is true.’

Also, if we buy into the former idea of the convex hull of ideas, afterward at several item in the near forthcoming it volition be unattainable to create advancement in math without a individual adding a new idea, a new definition, a new idea that creates a new spike in the polytope, and afterward advancement can occur.

“Will lone the `brightest minds’ be capable to meaningfully contribute to investigation mathematics?” At any stated period in the former of mathematics, there have been mathematicians who are investigation active, and whose mental capability for math seems entirely excellent individual (e.g., the owner of this blog, among many others). It is natural to surmise that they could resolve any issue we could solve, in a fraction of the period it would obtain us to complete a evidence and compose it up. However, this has never stopped those of us alongside a additional humble capability for math from enormously enjoying doing research, and producing results that are far from insignificant. In fact, math has continually benefitted from the range of ideas and points of view, from the extremely tangible to the big bird’s eyeview, from the smaller contributions to the construction of complete new theories.

Similarly, I am not threatened by the mathematical capability of LLMs. For one item their capability is currently limited, as pointed above. And for another, equal if their capability becomes far superior, there volition continually be a need for mathematicians at all levels to guide investigation in paths that create awareness for humans to stroll (not run).

The mathematical cosmos is enormous (as Emily Riehl said), and computing period is finite. There volition continually be areas of math that are under-explored and anywhere equal beginners can interrupt new ground. The LLMs can assistance in the process, by quickly exploring avenues that may be deceased ends, pointing out paths that have already been explored, and glowing a ray on paths that are apt to be fruitful.

As I mentioned above, I have never been this busy, since the admission to LLMs has multiplied the figure of areas that I have admission to, and my curiosity has expanded fine beyond my investigation area. I now have many additional ideas that I can perchance examine on my own, so I am recruiting additional pupil collaborators than always before, to assistance me test whether these problems can guide to engaging results. Students can be engaged in investigation before than always before too since the LLMs can assistance them study matter faster (and deeper!), by morality of being accessible 24/7 to answer their questions, alternatively of my meager one or two accessible hours per week to encounter alongside them.

“Will mathematicians be needed? Will they be employable?” I discover these questions natural but additionally perplexing. Even in the most dystopian of scenarios anywhere AI becomes excellent individual in all investigation tasks, what fine would a evidence (of a theorem in clean mathematics) be if there are no individual mathematicians to digest it and comprehend it? Regardless of the advances in LLMs and AI, there volition be mountains of investigation to be understood by humans, alongside or without the assistance of a computer.

In addition, we appear to ignore that math departments be in universities to assist two chief goals: discovery and communication of mathematical knowledge. Virtually all math division emphasizes, in equal parts, our investigation and instructional missions (and many institutions location the instructional goal of math at a much higher flat than their investigation mission). Mathematics courses are an integral part of a broad arts syllabus since learning to think as a mathematician is a extremely helpful and applicable skill. The fact that we are researchers adds huge value to our instructional goals, since students are finest served learning from those scientists who are in the frontlines of research. The investigation opportunities that we provision for undergrads are a extremely precious add-on to their curriculum, as it is a distinct category of training that helps them be employable in the future. And as lengthy as the mathematical way of thinking continues to be a extremely precious accomplishment to be learned by the undergraduate population, there volition be a awesome need for mathematicians to be hired by universities.

The LLMs are making math investigation additional accessible than always to those who are not equal in academia or equal mathematicians. This method that undergrads volition be capable to associate genuine mathematician-led investigation projects much additional easily, and it may be a new productive dirt for exploration. Not mindless exploration, though, but mathematical exploration anywhere the goal is understanding and for the students to be initiated and trained into a extremely specialized site (in an ethical way). And, of course, we should prioritize training students in how to communicate the math they learn, as that has continually been (and likely volition rotate into equal additional of) a crucial skill.

All of this to say that I cannot conceive that the LLMs volition displace mathematicians from their jobs. On the contrary, they power create jobs since our investigation efficiency may sky rocket. On the another hand, I am additional concerned concerning policies and backing issues that are governmental in nature and have nothing to do alongside the AI and LLM conversation.

Finally, the most crucial inquiry of all, that I wanted to location here:

“Should I prosecute a PhD in math at this time?” The answer to this inquiry have to be individual to all and all student. But, in my opinion, the answer should not have changed from a twelvemonth ago to today. The most crucial logic (and perchance the lone reason) to do a PhD in math have to be that the applicant is passionate concerning math and wants to rotate into an expert in a particular topic inside our field. If that is the goal, afterward the existence of LLMs in math is irrelevant, since the goal is achieved whenever the applicant has gained adequate cognition to be an expert on a particular problem. If anything, LLMs may be used as a tool to accomplish that goal additional efficiently. For one, I am using them all day to eventually comprehend concepts and techniques that I continually had questions about, and now I can query an LLM until I am completely satisfied. I am capable to hunt for the explanations and examples that click alongside me, that click alongside my own particular way of thinking concerning mathematics.

To what flat a pupil wants to use LLMs in a math PhD have to be a individual choice but I volition say that, as my coworker Jeremy Teitelbaum put it in a latest discussion (here is the bit I am referring to, and current is the complete interview), students cannot oversee not to study concerning the current capabilities of LLMs, or any another innovation for that matter. If your goal is to rotate into an expert, afterward you have to be amenable to learning from all experts in the field, and from all sources that may authorize you to go deeper into a topic than anyone alternatively before you — and LLMs can be extremely productive tools to examine literature, for instance.

But formerly again, the decision to do a PhD should not be according to the current province of the art of technology.

I wanted to do a PhD in math since it seemed akin a magnificent challenge. I wanted to do a PhD since I wanted to study how Andrew Wiles proved Fermat’s Last Theorem. I wanted to continue studying math since I merely did not desire “a genuine job,” and the chance of contemplating advanced math on my own for a few years seemed akin a aspiration to me, fair too fine not to provision it my finest shot. I cognize I would have deeply regretted it if I had not tried to complete a PhD whenever I had a chance (the finest period to do it is whenever your undergrad cognition is fresh!). I wanted to comprehend mathematicians conversation concerning math, and rejoice in the small small particulars and miracles that create proofs work. I wanted to encounter and suspend out alongside another group who additionally idea figure theory was the coolest item on Earth. I wanted to publish a document in a investigation journal, alongside my name on it, since I discovered a new theorem that no one had idea of before. I wanted to explain and portion my enthusiasm for math alongside others in a classroom and exterior of the classroom.

Simply put, I fair wanted to do math, and I would have been devastated if several undefined danger to the site of math frightened me distant from the chance to prosecute a PhD.

And if you are a pupil that is passionate concerning mathematics, and person who wants all of that too, afterward a PhD is the correct way for you, despite of the innovation accessible during your degree. You volition study to use the innovation to a flat that you are comfortable with, and that fulfills your own dreams and expectations of what a PhD in Mathematics method to you.

Afterword: the BIG OpenAI release. After I completed penning this blog post, and had already sent it to Terry, OpenAI released a huge treasure trove of results in mathematics. This is, undoubtedly, a historic period in mathematics. The theorems in their document demonstrate several huge open problems in mathematics: the resolution of the so-called quasi Riemann Hypothesis, Goldfeld’s conjecture, the Hodge Conjecture in the case of CM abelian varieties, Hilbert’s 10th complete Q, the Rigidity Conjecture… and the catalog goes on and on.

But specified a enormous publish does not power me to alter any of the points I made above. On the contrary, we already knew their models can do amazing things (e.g., Navier-Stokes). We already knew the frontier models can nexus dots in the existing writings in ingenious ways (e.g., unit-distance conjecture). We already knew that OpenAI can expend a mind-boggling amount of resources to assault problems.

Also, we suspected that their models have limits and the new publish shows evidence of that too. In their report, they citation that they attacked 4000 open problems, and their example was capable to create advancement on concerning 700 connected problems. Yes, several of the ones they were capable to resolve are huge. But it additionally shows that their models are limited, fairly perchance because of the arguments we explained above.

Are any of the solutions using new ideas that are exterior of the convex hull of the current ideas in the literature? We volition need mathematicians and period to digest these new proofs and comprehend what connections are being made, and whether brand new ideas were really discovered in the process.

The chief item of my article remains the same, though. There is a lot of mathematical investigation that remains to be done alongside and without the aid of LLMs. There are new mountains of math to explain and communicate to others. And if you are a pupil who is passionate to study what is new and what is remaining to do, afterward a PhD is certainly the correct way for you.

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Why annoy alongside C in 2026? It is, he said, motionless a awesome language. C is portable, stable complete the lengthy term, offers accelerated compilation, and the resulting binary code is fast. "What you see is what you get"; it is uncomplicated to appearance at C code and have several idea of what the device will actually do. There are a lot of tools for operating alongside the language, and C gets out of the way whenever necessary.

C does have a lengthy history, and that affects the tongue as we see it today, he said. The C89 norm had to oversee alongside a broad assortment of hardware, including machines alongside signed-magnitude or one's-complement integer representations, segmented memory, exotic pointer representations, and amazing sizes for types. Some Honeywell machines, for example, had nine-bit bytes. That greatly complex the project of penning a standard that would allow the penning of portable code.

The method that was taken was to define the semantics of the tongue in terms of an theoretical machine. All operations are to be executed as if they had run on that theoretical machine, which may not exactly equivalent the actual hardware. The observable behavior of the program must be what the abstract machine would have done. The "observable" part matters: admission to volatile variables, being defined as observable, must happen exactly according to the theoretical machine; everything alternatively fair has to produce the identical eventual result.

The norm gives a lot of liberty to compiler implementers; lone the observable behavior have to be preserved. There are many aspects of that behavior that are either undefined or implementation-defined. These are not observable behavior, and thus do not constrain what compiler implementers can do. There are, of course, another details that can constrain compiler developers anywhere the C norm does not; these contain ABI requirements, standards akin POSIX, or the need for backward compatibility.

Undefined behavior comes concerning whenever a program does item that is either not portable or not defined by the norm at all. In specified cases, the C89 standard states that it "imposes no requirements" on the implementation. Undefined behavior exists for a figure of reasons. It allows implementations to assistance extensions, manage interactions alongside hardware-based safety mechanisms, and execute aggressive optimization, all during allowing difficult-to-detect errors to be ignored. It explicitly gives the compiler the correct to disregard entire classes of hard-to-detect errors.

Nasal demons

The problem, Uecker said, is that the norm allows a compiler to do anything in reply to undefined behavior, up to the item of invoking nasal demons. If a program contains any undefined behavior at all, according to compiler writers, afterward it has no expected semantics. The C++23 standard goes additional to explicitly province that the norm imposes no requirements for these programs. That has led to general disagreements between developers concerning what can be expected from the language.

For example, if you zero an complete construction (perhaps alongside a call to memset()), then compose to particular fields, what volition happen if you peruse from any padding bytes in that structure? Might they merge security-relevant data? A 2015 survey showed that there was no agreement on what should happen in that case. Or regard this uncomplicated code:

 extern int x; int f(int a, int b) { x = b ? 42 : 43; return a/b; } 

If b is zero, afterward the come back declaration is a division by zero, which is undefined behavior. In this case, is the compiler entitled to omit the test entirely and fair execute x = 42? After all, the b = 0 case has no expected semantics, and can thus be ignored. There are compilers that volition do exactly that. In the undefined-behavior case, the shop to x is not observable behavior. But now consider this case:

 extern void g(int x); int f(int a, int b) { g(b ? 42 : 43); return a/b; } 

This power appear to be the identical situation, alongside the compiler being entitled to eliminate the test and fair continue 42 to g(), and several compilers have treated that way — but that compiler behavior was a bug. Imagine a definition of g() that calls exit() if b is zero. In that case, the division volition never happen and the program's behavior is not undefined. So eliding the test and merely passing 42 to g() is incorrect.

One additional engaging case:

 volatile int x; int foo(int a, int b, bool store_to_x) { if (! store_to_x) come back a/b; x = b; return a/b; } 

The inquiry current is: can the compiler hoist the final division operation above project to x? If there are no semantics connected with the b = 0 case, afterward there is no alter in observable behavior. This, too, is item compilers have done, but the C23 norm added a "no period travel" stipulation to disallow it. In C++, instead, hoisting must be explicitly prevented by inserting a call to std::observable_checkpoint().

Time-travel bugs should eventually go away, but there are a lot of other situations where, equal if the norm is clear, compiler writers often disagree. These contain study of uninitialized variables (which is almost continually defined), and equality comparisons of pointers, which is continually defined, but is additionally miscompiled by the two Clang and GCC.

Fighting undefined behavior

To try to location all of these problems and more, the C commission runs three study groups focused specifically on the recollection entity model, memory safety, and undefined behavior. There are currently concerning 100 instances of undefined behavior in the C standard, but the in-progress C2y outline has removed 45 of them. The circumstance is certainly getting better.

There is an increasingly affluent set of tools aimed at finding issues: compiler warnings, fixed analyzers, sanitizers, LLM-based tools, formal verification, and more. The figure of situations anywhere a compiler will emit a alert anywhere imaginable undefined behavior is detected is growing; recent examples contain improved warnings for entire figure overflows and potential use-after-free situations. Static analyzers are accessible as standalone tools, but are additionally increasingly being built into the compilers themselves; GCC can now notify concerning a figure of possible buffer-overflow situations, for example. Sanitizers activity by inserting run-time checks; they can catch a lot of undefined behavior and, in trapping mode, be used for hardening as well.

Memory safety has never been one of C's powerful points, but Uecker wanted to make the item that it can be improved. That issue breaks downward into three sub-problems: category safety, spatial recollection safety, and temporal memory safety.

C, he said, has a powerful category system, and the remaining problems are fixable. Tagless unions, for example, can create category confusion, but the compiler can enforce types alongside several additional annotations. New diagnostics can capture unsafe casts from void. Type checking across translation units is traditionally not a huge issue in C, since header records are used to justify accordant types, but the circumstance could be improved alongside a link-time checker.

Spatial recollection safety — limits checking — is a partially solved problem; the compilers can execute array-bounds checking in many situations now. In some cases, several code changes are needed to completely advantage from this checking. Use of the counted_by attribute can allow checking for elastic gathering members, for example.

Temporal recollection safety — avoiding use-after-free bugs and the akin — is harder, Uecker said, and Rust certainly has an advantage there. Still, better temporal memory-safety implementation is possible. Architectures like CHERI can assistance current is well. Fil-C can discover a lot of temporal-safety bugs.

Can all of these tools and tongue changes get us to complete recollection safety? Completely solving the issue volition necessitate either costly run-time checking or ceremonial verification, he said. In the near future, the most complete results volition be had alongside the blend of a restricted language and ceremonial verification tools.

Overall, he concluded, C is motionless a living tongue and is motionless improving. The C23 norm removed a figure of problematic features, including old-style (K&R) function definitions, assistance for sign-magnitude and one's-complement machines, and trigraphs. It added bit-precise integer types, checked entire figure operations, and more. C2y volition go further, adding case ranges, named for loops, the _Countof() macro to determine gathering lengths, and a lot of "demon removal". It volition not achieve complete recollection safety for C, but that is an eventual possibility, and will rotate into additional applicable complete time. He ended by encouraging interested people to act in the operating groups.

The video and slides from this talk are available.

[Thanks to the Linux Foundation, LWN's journey sponsor, for supporting my travel for this event.]

Three years ago, Menlo Ventures partner Matt Murphy helped guide among the earliest investments in Anthropic. Menlo volition be among the biggest winners whenever the business goes public, alongside a interest estimated at about $14 billion. Before the IPO, Murphy sits downward alongside Chief Correspondent Ben Bergman to conversation how the prosperity flowing into these important AI companies is impacting the broader backing environment, and anywhere he's looking for the next big opportunity.

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git copy https://github.com/edrisranjbar/lifeos.git && cd lifeos cp .env.example .env # set your sign-in and passwords docker create up -d

Open localhost:8080 and sign in. MySQL, the app and your attachments all live in their own volume, so docker create up -d --build following a git drag upgrades without losing data.

Download lifeos-<version>.zip from the latest release and extract it into your hosting home directory, so public_html/ becomes your web base and the backend sits safely complete it. Copy config.example.php to config.php, inhabit in your MySQL details, and open your site. See the deployment guide for details.

You need PHP 8.1+ (with pdo_mysql and mbstring) and MySQL 8+ (or MariaDB 10.4+).

git copy https://github.com/edrisranjbar/lifeos.git cd lifeos cp config.example.php config.php # PowerShell: Copy-Item config.example.php config.php

Edit config.php alongside your repository association and the username and password you desire to sign in with, afterward commencement the server:

php -S localhost:8000 -t public_html server.php

Open localhost:8000 and sign in. The repository and tables are created on archetypal use. You can alter your sign-in afterward in Settings.

A personal /api/v1 API covers the dashboard, goals, tasks, habits, finances and sticky notes. It uses its own transporter token, distinct from your browser sign-in.

php -r "echo bin2hex(random_bytes(32));"

Set the outcome as api_token in config.php (or the LIFEOS_API_TOKEN surroundings variable), then:

curl -H "Authorization: Bearer $LIFEOS_API_TOKEN" https://your-host/api/v1/dashboard

See the API reference for endpoints, payloads and errors.

cd mcp npm ci cp .env.example .env # set LIFEOS_BASE_URL and LIFEOS_API_TOKEN npm start

The server runs complete stdio; there is no hosted MCP endpoint. The MCP guide lists all tool and shows client configuration for Claude and Codex.

• Everything lives in your MySQL database. Habits use dedicated tables; another workspaces shop JSON documents in app_state.
• Saves use revision checks, so a old tab can't silently overwrite newer changes. API writes use transactions and row locks.
• Data from older browser-only versions is imported once; conflicting copies are archived as legacy_backup_* records.
• Back up the database, attachment records and your configuration together.

Moving from the old Habittify SQLite database? See migrating Habittify.

Edi Life OS is built for a single owner.

• The API token grants peruse and compose admission to all supported domain. Token scopes and app-level charge limiting are not implemented, so set petition limits at the hosting layer.
• Use HTTPS anyplace another than localhost, and keep tokens out of URLs, frontend code, screenshots and logs.
• Secret notes are hidden from API says by default. The concealed emblem controls visibility; it is not distinct encryption.

public_html/ Browser workspaces, shared resources and Apache admission points lib/ API domains, reply helpers and province transactions mcp/ Optional Node.js stdio MCP server tests/ Timer, API, MySQL and integration checks docs/ Guides, affirmative README screenshots in docs/media config.example.php Configuration template (no genuine credentials) server.php Development router and use routing api.php HTTP API admission point 

Built alongside PHP, MySQL and vanilla JavaScript — no frontend build step, no framework.

Dockerfile, docker-compose.yml and docker/config.php run the app alongside configuration from surroundings variables.

• API reference — authentication, routes, data formats and concurrency
• MCP guide — tools, surroundings settings and client setup
• Deployment guide — Apache, shared hosting, backups and migration
• Development guide — local checks and available integration fixtures
• Growth workspace · Debts and credits

MIT © 2026 Edris Ranjbar. Use it, fork it and build on it.

Ideas, bug reports and drag requests are welcome. Open an issue to commencement a conversation, and see the development guide to run the checks locally.

Indeed, at smallest two group asked if I scheme on sending Sam Altman a flask of whisky, as promised in my Problems page. The answer to that is no. And let me explain to you why, and why you have to be extremely furious at OpenAI.

Let me explain item first. I am not entirely against the use of AI. I comprehend that it is a tool, and many group discover it useful, and I am blessed to concur that it is merits. I think that we need to improved comprehend how this innovation changes our site and how we desire to use it before we hurry to throw our lot alongside it. This is why I mostly evade using AI for math (I am blessed to ask LLMs to consolidate data for me, or to create a helpful infographic, or to evidence peruse an email, etc.), it's fair the example of using it that is currently missing in my view. Still, we are in the phase of discovery, group use it and we see the consequences unfold and we can decide as a community to clasp it additional or restrict its use. For example, arXiv introduced charge limiting as a consequence of these tools, and nearly all document should contain several AI declaration at this item (even to province that no AI was used, I guess). We need to decide how we fairness the contribution of the author, e.g., do we necessitate the conversation log be made public, or at smallest accessible to the reviewers and the editor? These are questions that we need to contend alongside and see anywhere they obtain us.

But, rear to the Partition Principle. I took a concise appearance at the preprint released by OpenAI (not at the Lean code, since I cognize extremely small of the genuine use of Lean, and that code was enormous). It sucked. It is unclear, muddled, and has a different structure. The terminology is "a bit off", there are theorems that I would not anticipate to be proved, that are additionally stated in a extremely different manner. Lemma 7.4, again, is not item you'd anticipate to see in a document akin this, the identical goes to Lemma 8.1.

Then there are the references. One of my document on the Bristol example is cited for several essential introduction of symmetric extensions, which is not the citation I would have chosen; at smallest three unpublished, unrefereed, and non-arXiv'd address notes are used as references (including mine, which is used to mention to one proposition that I am certain appears in several published document of quarry and of others). That's not what you'd anticipate from a grave preprint claiming to resolve a problem.

Let me difference this preprint to any document in set theory that isn't dealing alongside the technicalities of a subfield I'm not acquainted with. I can nearly certainly skim a set theory document and have a fine idea as to what is going. I power not be acquainted alongside several of the terminology, or the arguments, but for the non-technical stuff, I have to be capable to comprehend the scheme and the construction. And for document on my expertise, on the Axiom of Choice, I volition mostly need to skim the document briefly to have a fine idea as to what the authors did (or intended to do). Not to say that I am continually right, or that I continually get it exactly as the authors intended, but this is what I've spent complete 15 operating on, and by now I have a beautiful fine idea how it works. But alongside OpenAI's document on the Partition Principle this is impossible.

No, if this was an scholarly document submitted to a journal, it have to be issued a array rejection for the quality. The onus is continually on the author to conform and adhere to the communications level. Much akin a document written in Swedish would apt be rejected outright from the Proceedings of the American Mathematical Society, since the onus is on the authors to create it accessible for the readers. And therein lies the rub.

OpenAI drops several hundreds of "solutions", incomprehensibly written "solutions", and we are all expected to jump on them and what? Appreciate their contributions? Why? I am trying to complete multiple papers, I am supervising a figure of Ph.D. students, and I have a lot of energetic investigation of my own to do. When am I expected to sift through a seriously written paper? Why should I bother, whenever they don't annoy to communicate better?

The OpenAI media publish fair suggests "progress". But the media cycle, for everyone else, these are "solutions". This is a ideal way to have your pie and eat it too. But it is the equal of a Denial of Service, should we desire to obtain it seriously: halt everything alternatively and fig this one out. And perchance several group worked for years on a issue that they power fair do that, but as a whole... Well. If this was genuine attempt to portion progress, the norm of the penning would be better, it would obtain several input from genuine experts on the genuine topic. OpenAI is a occupied company, but I am certain that if they desire to be genuinely helpful, they can discover the period to communication a few mathematicians for direction and input, if nothing else, afterward on the norm of the outputs. Because equal if this is a huge jump onward in improvement, it is not nearly at the satisfactory level. But hey, everyone can assertion that ChatGPT solved a long-standing issue (this one, concerning the Partition Principle, is complete a hundred years old), so why should OpenAI care?

And what are we, the mathematicians, remaining with? We are remaining alongside a public, backing bodies, and guideline makers that are not academics, not experts, not mathematicians, nor they are acquainted alongside how mathematical investigation is done (since it is extremely distinct from investigation in biology, psychology, or philology), who see these "results" and think that we can substitute mathematicians alongside bots. This is the equal of replacing the chef alongside the galley tools, equal if the blade is automatic and can cubes veggies on its own, there is motionless a important extend between that and palatable food that you'd desire to eat.

It seems to me, that the AI tech companies would akin us to conform to their standards, fairly than expend the period and energy to conform to ours. And I am fearful that this fair volition not do. So, no, I volition not be sending Sam Altman a flask of whisky anytime soon, nor I am preparedness on spending my period study through that document and trying to create awareness of it.

Want to comment? Send me an email!

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