The Founder's Office Intern at Easydesq plant anywhere a company's agency hunt turns into a signed lease. You activity beside the founders to assistance start-ups, Small and Medium Enterprises (SMEs) and corporates discover business space, afterward shove the conditions in their favour. Every construction you shortlist and all clause you enhance affects what a client pays all month.

This guide is for students and caller graduates preparing for this on-site function at Easydesq's Saket agency in New Delhi. It covers what the activity looks akin day to day, the skills the founders appearance for, the apt discussion rounds, 28 role-specific questions alongside example answers, and a four-week preparedness plan.

1. About the Founder's Office Intern Role

In plain terms, you are a younger business genuine asset adviser alongside straightforward admission to the founders. A client power say they need area for 40 group near a metro position in the Delhi National Capital Region (NCR), inside a fixed budget.

You discover suitable buildings and stroll the client through them. You afterward assistance agreement on rent, deposit and lock-in until the two sides sign.

Your period is divided between the agency and the field. At the desk, you activity alongside the founders on pricing and agreement strategy. In the field, you encounter businesses looking for space, construction owners, and another brokers who cognize which floors are concerning to rotate into vacant.

2. Required Skills and Qualifications

This function is hired chiefly on outlook and group skills. The founders volition fairness all accomplishment below from your answers and from how you transport yourself in the room.

Skills Overview

3. Day-to-Day Responsibilities

A representative week mixes array activity at the Saket agency alongside period in the site throughout Delhi NCR. Mornings frequently go to calls, investigation and client updates. Afternoons go to location visits and meetings alongside owners and clients.

4. Key Competencies for Success

Many candidates can depict negotiation and revenue in theory. The ones who remain out display the habits that keep deals moving in a small, accelerated team: preparation, follow-through, honesty, dependable nerves and the capability to study quickly from the founders.

5. Interview Process at Easydesq

Easydesq is a young, founder-led team, and hiring at firms akin this is normally abbreviated and practical. Expect the founders to be closely involved.

Also anticipate at smallest one instant anywhere you have to sell, convince or negotiate on the spot. The sequence below reflects how revenue and leasing internships at founder-led firms are commonly assessed.

6. Interview Questions

Answer behavioral questions alongside the STAR method (Situation, Task, Action, Result), and create the Action part the longest. For leasing, market and case questions, province your assumptions, display calculations stage by step, and keep your construction Mutually Exclusive, Collectively Exhaustive (MECE).

Founders hiring for revenue roles attention small concerning textbook answers. They attention additional concerning whether you audio akin person a client would rely alongside their money.

7. Topics and Areas of Focus

You do not need previous genuine asset experience, but you do need operating fluency in the basics. Each area below maps to a part of the job you volition do in your archetypal weeks, and to the benevolent of questions and role-plays you can expect.

8. Preparation Plan

Pre-Interview Checklist

9. Career Growth & Next Roles

In business genuine asset advisory, progression is driven mostly by results: the deals you assistance near and the relationships you build. In a founder-led firm, interns who origin fine leads and grip clients fine lean to get duty quickly, since there are few layers between them and the founders.

The timelines below indicate representative paths in leasing advisory and change alongside idiosyncratic performance.

10. Conclusion

If you recall one thing, create it this: the founders are hiring person who volition sell. Every answer, from "Why Easydesq?" to the role-play, should display that you bask persuading group and grip rejection without losing energy.

It should additionally display you can be trusted in forefront of a client. A polished scheme throw without that evidence is improbable to be enough.

Two additional actions volition set you apart. First, create lease arithmetic automatic, so total outgo, productive rent and disbursal per usable sq ft arrive out quickly and correctly under pressure.

Second, stroll through an agency district specified as Saket District Centre or Nehru Place before the interview. Bring rear one genuine study concerning the buildings, tenants or vacancies you saw.

Top Tips for Interview Success

A Management Consultant Intern at IRESH Consultants plant on live agreement and growth mandates. The activity includes researching targets for a mergers and acquisitions (M&A) transaction, finding investors for a fundraise and evaluation a client's go-to-market (GTM) plan. The numbers and slides you build can attain a promoter or investor, so accuracy carries genuine weight.

This guide is for MBA students preparing for this internship. It covers what the function involves all day, the skills interviewers test and a apt discussion sequence. It additionally includes 29 role-specific questions alongside answer approaches, the topics to expert and a four-week preparedness plan.

1. About the Management Consultant Intern Role

This internship blends administration consulting alongside funding banking support. IRESH Consultants advises businesses on M&A, equity and debt fundraising, small and average endeavor (SME) first community offering (IPO) readiness, strategic consulting and growth initiatives.

As an intern, you create small but crucial calls. You decide which data is credible, which investors or targets create the shortlist and how a finding is framed before a elder adviser takes it to a client.

You activity closely alongside elder consultants and sit in on client meetings and inner reviews. Expect to toggle between workstreams in the identical week, specified as a evaluation example one day and a guide catalog or GTM scheme the next.

2. Required Skills and Qualifications

Educational Qualifications

Skills Overview

3. Day-to-Day Responsibilities

A representative week mixes array research, Excel and PowerPoint activity alongside client-facing moments specified as meetings and outreach. The activity falls into five areas, and all project below can district on your array during the internship.

M&A Advisory and Fundraising

Business Development and Market Research

Financial Analysis and Valuation Support

Growth Strategy and Go-to-Market

Client Engagement and Project Execution

4. Key Competencies for Success

Strong Excel and communication skills get you shortlisted. What separates top interns in advisory activity is judgement: knowing which figure matters, which guide is value chasing and how to say item difficult to a promoter.

5. Interview Process at IRESH Consultants

Boutique advisory firms that employ MBA interns normally run a abbreviated procedure led by elder consultants and partners. Prepare for a sequence akin the one below. Confirm the exact rounds and timelines alongside your placement cell, since firms frequently modify the format by campus.

6. Interview Questions

Answer behavioral questions alongside the STAR method (Situation, Task, Action, Result). For case questions, use a mutually exclusive, collectively exhaustive (MECE) structure, and for specialized questions, stroll through the calculation aloud.

Advisory interviewers prioritise accuracy alongside numbers, a apparent construction and a advice at the end. Practise saying your answers out loud, not fair study them.

7. Topics and Areas of Focus

Master these areas before the interview. Each one maps to a part of the activity you volition be tested on, from evaluation to GTM.

Prepare from chief sources anywhere possible. Useful places to commencement are business annual reports, business expert data on the Ministry of Corporate Affairs portal and chief market regulations on the Securities and Exchange Board of India (SEBI) website.

8. Preparation Plan

This guide covers anywhere Easy DESQ came from, how its endeavor is set up today, and what it offers, so you can stroll into an discussion alongside the company's genuine narrative fairly than a guess.

1. Company Overview

About Easy DESQ

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Company History

2. Mission, Vision & Values

3. Products and Services

4. Key Competitors of Easy DESQ

Easy DESQ competes in outsourced back-office services for Indian small businesses and startups. Founders hand off bookkeeping, GST and income-tax filings, payroll and ROC compliance alternatively of construction an in-house backing team.

Customers choose a provider on a few tangible factors. These are breadth of services under one roof, degree of advisory beyond regular filings, pricing model, speed of response, and how much a originator trusts the squad alongside delicate financial data.

In an interview, difference Easy DESQ alongside all competitor on the factors above: assistance breadth, advisory depth, pricing, turnaround and trust. Then explain which client section all participant wins and why, fairly than reducing the answer to who is bigger.

5. Corporate Social Responsibility (CSR)

For a expanding business akin Easy DESQ, duty shows up mostly in how it hires, trains people, handles client data and runs regular operations, fairly than in ample foundation-led programmes. Section 135 of the Companies Act, 2013 makes ceremonial CSR spending mandatory lone complete defined financial thresholds, so the themes below are the areas anywhere a endeavor of this benevolent typically contributes.

Before you name a particular program in an interview, inspect Easy DESQ's latest website, LinkedIn leaf or media communication so your example is current and accurate.

6. Interview Process & Selection Rounds

Hiring at Easy DESQ, akin at most expanding Indian companies, normally runs through a abbreviated sequence of screening, functional and fit rounds. The hiring squad decides the exact mix for all opening.

Sales roles lean on role-plays, innovation roles on coding or problem-solving tasks, and backing or HR roles on applicable compliance questions. Senior hires typically encounter leadership, during fresher and evidence drives may add an aptitude test. Location decides whether rounds happen in individual or complete video.

7. Common Interview Questions Across Roles

Any Easy DESQ applicant can visage these questions, any the function. Role-specific specialized questions live in all role's own discussion guide.

8. Required Skills & Tools

Easy DESQ has no sole catalog of required skills: all occupation path asks for a distinct mix, and the rows below use the identical path names as the Career Opportunities division that follows.

9. Career Opportunities at Easy DESQ

Careers at Easy DESQ are finest understood as functional routes, the identical ones mapped in the Required Skills array above, all alongside its own admission function and promotion ladder. Most group associate at administrator or affiliate flat on one path and move up by owning bigger accounts, systems or teams, fairly than by switching tracks early.

10. Future Outlook and Strategic Plan

Easy DESQ's current positioning is finest peruse through the milestones in the Section 1 timeline. Each milestone shows what the business chose to build on next.

The five themes below are directions to comprehend and conversation in an interview. They are not forecasts or targets, and you should not current them as such.

11. Conclusion

Easy DESQ operates in a rivalrous area anywhere the rivals profiled before vie for the identical clients. Its position rests on the assistance lines and milestones covered in this guide.

Its direction depends on keeping client cognition accordant during processes and innovation mature. It additionally depends on applying its stated values as the squad grows.

For person joining, that method being judged on ownership and assistance quality. Candidates who nexus their own cognition to the company's history, values and rivalrous stance volition remain out complete those who lone narrate facts.

Iresh Consultants is a consultancy, and preparing for an discussion there starts alongside one exact question: what does the resolute really sell, and to whom? Consultancies win activity on ability and rely fairly than on a bodily product. Interviewers hence hear closely for whether a applicant understands the firm's particular assistance lines and client base, or is merely repeating a generic idea of "consulting".

Your strongest preparedness is first-hand research. Read the firm's own website and inspect its authoritative business registration record. Then nexus your cognition to the particular problems its clients bring to it. A applicant who can depict the firm's activity accurately, in its own terms, stands out quickly.

This guide covers anywhere Iresh Consultants came from, how it is organized today, and what it offers clients.

1. Company Overview

About Iresh Consultants

Iresh Consultants operates in the consulting space. That makes it a helpful case study for anyone applying to a client-facing or shipment function there, since the firm's position depends on how its group range problems, communicate alongside clients and provision on commitments.

Before the interview, verify the firm's lawful particulars on the Ministry of Corporate Affairs portal. Its business expert data shows the incorporation date, registered office, current position and directors. These are the identical basics an interviewer expects you to cognize without prompting.

Company History

For any consultancy, the former value knowing is structural. Look at who set it up, whether ownership or board have changed, and whether it has added or dropped assistance lines. Interviewers investigation these points since they explain how the resolute sells and staffs its activity today.

Comparing the board listed in the MCA document alongside the guidance named on the authoritative website is a quick way to see whether authority of the resolute has changed.

4. Key Competitors of Iresh Consultants

Iresh Consultants competes in India's recruitment and staffing services market, anywhere companies hand external partners their hiring mandates alternatively of filling all function in-house. Client firms rarely choose a consultancy on brand alone. The deciding factors are speed to a norm shortlist, field depth, how many placed candidates stay, fee construction and the capability to inhabit roles throughout cities.

The rivals below are firms a hiring director weighing Iresh Consultants could realistically concise instead, for imperishable placement, agreement staffing or quantity hiring.

In an interview, evade treating "who is bigger" as the answer. Compare Iresh Consultants alongside one competitor on two or three genuine dimensions, specified as field focus, turnaround period or applicant retention, and afterward explain anywhere a smaller, focused consultancy can win a client a ample network cannot.

5. Corporate Social Responsibility (CSR)

For a consulting business, the most dependable social contribution comes from what the resolute already does well: construction skills, sharing know-how and assisting organisations and group activity better. The themes below prosecute from that endeavor example and display anywhere a consultancy can contribute.

Check Iresh Consultants' latest website and social media updates before you name a particular program in an interview. Quoting a current, genuine undertaking is far stronger than describing a generic one.

6. Interview Process & Selection Rounds

Hiring at Iresh Consultants follows the applicable form average to Indian consulting and services firms: a screening stage, a skills check, afterward deeper conversations alongside the group you would activity for. The command and the figure of rounds can alter alongside the role.

Freshers can anticipate additional accent on aptitude and communication. Experienced hires are judged chiefly on former activity and domain depth. Technical, client-facing and assistance roles all location the importance on a distinct round.

7. Common Interview Questions Across Roles

Any Iresh Consultants applicant can visage these questions, any the function. Role-specific specialized questions are covered in all role's own guide.

8. Required Skills & Tools

Iresh Consultants does not have one catalog of required skills: all occupation path needs a distinct set, and the array below uses the identical routes as Career Opportunities.

9. Career Opportunities at Iresh Consultants

Careers at Iresh Consultants are finest understood as distinct routes, all tied to a distinct category of client or inner work, fairly than as a sole company-wide ladder. Each path has its own admission titles and its own idea of what "ready for the next level" looks like.

10. Future Outlook and Strategic Plan

The milestones in the Section 1 timeline display Iresh Consultants construction its stance stage by step. The resolute grows by earning client rely and widening its ability one involvement at a time. Its direction rests on five themes, all of which gives you item tangible to conversation in an interview.

11. Conclusion

Iresh Consultants competes in a services market anywhere firms win on trust, ability and the norm of all engagement, not on size alone. Its direction depends on how fine it keeps clients returning, how deeply it builds expert knowledge, and how fine its group clasp improved tools without losing rigour.

For person joining, that method apparent work: what you create reaches clients directly, and your judgement shapes the firm's reputation. Candidates who nexus their cognition to the company's history, values and services volition remain out from those who lone narrate a CV.

Candidates normally arrive throughout Runtime Solutions on a job portal, a evidence placement catalog or a recruiter's message. Before any interview, pin downward exactly which endeavor sits rearward that name.

The name suggests a innovation focus, but "runtime" is a average application term and appears in additional than one company's branding. Confirm the registered entity, its board and its genuine row of activity from chief records alternatively of guessing from the brand.

That groundwork matters additional than it seems. Interviewers at innovation firms frequently ask what you cognize concerning their clients, how they provision activity and why you chose them complete a larger employer. A exact answer built from the company's own website and its statutory filings marks you out as prepared.

This guide covers anywhere Runtime Solutions came from, who runs it today and who it competes with, alongside a applicable method for checking all item yourself.

1. Company Overview

About Runtime Solutions

Runtime Solutions is a helpful case study since you have to established anywhere it sits in the innovation value chain. Find out whether it builds its own products, delivers projects for clients, places engineers alongside another companies or trains them.

Each example rewards distinct skills. A merchandise business probes ownership of features. A services resolute probes client handling and shipment discipline. A training or staffing endeavor probes communication and breadth. Read the company's own assistance pages first, afterward form your answers to the example you discover there.

For an Indian-registered company, the Ministry of Corporate Affairs (MCA) portal lets you appearance up the company's expert data liberated of charge. This includes its incorporation date, registered agency and current directors. Use it to verify you are researching the correct Runtime Solutions before you prepared item else.

-->

Company History

Interviewers rarely attention how old a resolute is. They attention whether you comprehend how it got to its current shape. Pay notice to changes in ownership, a change from one endeavor example to another (for example, from training into project delivery), or a move into a new innovation area.

Each of these explains why the business hires the roles it does today. Each additionally gives you a natural answer to "Why do you desire to associate us?"

2. Mission, Vision & Values

Take Runtime Solutions' declaration of intent term for term from its About or Careers page, and note whether it is a ceremonial mission, a tagline or recruitment copy. Quote it accurately in an discussion alternatively of paraphrasing it from memory.

Firms of this benevolent frequently depict their values in the wording on their careers leaf and in how they current client work. Prepare one particular narrative for all value the business really names, using its own vocabulary, not a generic catalog of teamwork, integrity and innovation.

3. Products and Services

The quickest way to comprehend Runtime Solutions' offering is to kind everything listed on its services pages into a few apparent groups. Then equivalent all collection to the function you are applying for. Engineering, delivery, pre-sales and endeavor betterment candidates volition all be questioned on distinct parts of that list.

• Core innovation offering: acknowledge the chief item the business sells, whether that is tradition application builds, phase implementation or ongoing use support, and be prepared to explain it in one sentence.
• Delivery model: activity out whether projects are run at a fixed price, billed by period and materials, or staffed through dedicated squad arrangements, since this shapes how interviewers fairness your evaluation and ownership answers.
• Industries served: note any sectors the business highlights, specified as banking, retailing or healthcare, and prepared one example from your own activity or studies that fits each.
• Technology stack: catalog the languages, haze platforms and tools named on job descriptions and case studies, since these are the most apt topics for specialized rounds.
• Training or endowment services: inspect whether the business additionally trains or places engineers, as this frequently explains the fresher-heavy hiring that many smaller innovation firms depend on.

4. Key Competitors of Runtime Solutions

Runtime Solutions competes in embedded systems and IoT merchandise engineering services. Here, OEMs, enterprises and public-sector buyers outsource hardware design, firmware, connected-device application and the haze tier that ties them together.

Buyers in this area rarely choose on size alone. They measure hardware-to-cloud depth, domain cognition in their industry, shipment cost, and how fine a partner handles certification and lengthy merchandise lifecycles.

In a competitive-analysis answer, difference Runtime Solutions alongside one or two of these rivals on particular dimensions: domain focus, hardware-to-cloud depth, disbursal of shipment and client type. Then explain anywhere a smaller, focused resolute can win against a larger one.

5. Corporate Social Responsibility (CSR)

A innovation services endeavor akin Runtime Solutions has its most dependable social effect through digital skills, learning and liable use of technology. The themes below prosecute from the benevolent of activity it does. They are not a catalog of confirmed named programmes.

Before you name a particular undertaking in an interview, inspect the company's latest authoritative website, LinkedIn leaf or media communication. Interviewers notice whenever a applicant cites a program that does not exist.

6. Interview Process & Selection Rounds

Runtime Solutions hires akin most Indian IT services companies. A abbreviated screening is followed by a skills inspect and one or two conversations that test the two competence and fit.

The procedure varies by role. Freshers normally visage a written or online test, during informed hires go direct to specialized or functional discussions. Sales, promotion and HR roles toggle coding assessments for case discussions and role-plays. Treat the rounds below as the applicable form to prepared for.

7. Common Interview Questions Across Roles

Any Runtime Solutions applicant can visage these questions, any the function. Role-specific specialized questions are covered in all role's own guide.

8. Required Skills & Tools

Runtime Solutions does not have one skills list. Each occupation path needs a distinct set, and this array follows the identical routes used in Career Opportunities below.

9. Career Opportunities at Runtime Solutions

Careers at Runtime Solutions prosecute shipment routes that mirror its assistance lines, so you develop inside a specialized or client-facing track fairly than through one generic ladder. Each path below matches the Required Skills array in Section 8 and names the titles you can anticipate at all level.

10. Future Outlook and Strategic Plan

Read against the milestones in the Section 1 timeline, Runtime Solutions presents itself as a innovation services partner that has grown by adding capabilities and client relationships stage by step. Its direction is clearest as themes, not targets.

Each theme below is item an interviewer can probe. Treat all cardstock as a talking item you have to be capable to rear alongside one tangible example from your own activity or coursework.

11. Conclusion

Runtime Solutions competes in a crowded innovation services market. It wins activity through shipment reliability and client trust, not brand measure alone.

Its direction depends on how fine it keeps gait alongside modernisation, data and automation petition during holding norm steady. That direction additionally depends on retaining group who study fast.

For person joining, the chance is broad visibility and apparent ownership first on. The anticipation is adaptability: learning new stacks quickly, communicating plainly alongside clients and treating all deliverable as your own.

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The identical commands as cargo, alongside blaze in front.
Blazingly accelerated Rust, literally.

Install · Try it · How fast · How it works · Docs

curl -fsSL https://raw.githubusercontent.com/indexable-inc/blaze/main/install.sh | sh

For macOS on Apple silicon and Linux, alongside or without Rust installed: blaze downloads the Rust it builds alongside formerly (about 100 to 135 MB), or uses rustup's. Rather use crates.io or Nix? Other ways to install.

In any Rust project:

blaze run # akin cargo run blaze test # akin cargo test blaze build # akin cargo build 

The archetypal build runs cargo, afterward waits a few seconds for blaze to burden your project. Then change a function and build again: it's rear in milliseconds, and the Finished row says [ra-jit] (blaze's engine). Every another cargo command passes direct through to cargo. Where cargo is installed, cargo blaze run is the identical as blaze run.

• Edits rebuild 10× faster on macOS arm64, 24× faster on Linux aarch64 and 11× faster on Linux x86-64 than alongside cargo build, on average complete genuine projects.
• About 20 ms from preservation a document to a operating program using the new code, with live edits.

Every project, platform, chart, and the recollection and disk it takes: benchmarks.

blaze runs beside cargo and keeps your project compiled in memory. After an edit it compiles only the functions you changed and writes them into the program cargo already built, alternatively of linking it again from scratch. A program that opts in can obtain the new code during it keeps running. More.

• macOS on Apple silicon and Linux. On Windows all build is cargo's.
• blaze is its own toolchain. It builds all project alongside its own Rust, blaze-2026-10-25 (today the nightly of that date), any the project pins; stable code and #![feature(...)] code build. Your toolchain and CI remain the final word (why).
• It trades recollection for speed: a few GB for a small project.
• It's young. Keep cargo inspect or your CI as the final word, and tell us whenever item differs from cargo.

All the details: what plant and what doesn't yet.

• Install: requirements, crates.io, Nix
• Using blaze: day to day, rebuild on save, making plain cargo build use blaze
• Benchmarks: all number, how it's measured, and on what machines
• How it works: a map of the code
• Cargo compatibility: all command, emblem and setting
• Hot reload: changing a operating program
• CI runner and roadmap: anywhere blaze is going
• Contributing

nix run github:indexable-inc/blaze

This opens a Game of Life window. On macOS on Apple silicon you can alter its rules, colours and speed during it runs; elsewhere the opening runs without live edits. It needs Nix, and builds blaze from origin the archetypal time.

For publish news: Watch → Custom → Releases on GitHub.

We're operating alongside a few teams on accelerated Rust CI and distant builds, built on blaze. If your team waits on Rust builds, inform us what slows you down: andrew@ix.dev. Teams we activity alongside get first admission and a straightforward say in what we build next.

How to build blaze from a checkout, run its tests and dispatch a change: docs/contributing.md. To study a safety problem: SECURITY.md.

blaze stands on the shoulders of giants. Thank you to the group who built:

• rust-analyzer, whose inspection and MIR blaze compiles from
• Cranelift (Bytecode Alliance), the accelerated code generator
• LLVM and inkwell, for the optimised tiers
• salsa, the incremental motor underneath
• object, for study and penning executables
• Rust and its compiler crates
• rustd (DioxusLabs), whose code blaze includes

None of these projects endorses blaze.

blaze was called ra-jit until 2026-10-24; cargo ra-jit motionless works. Made at ix for den's native app. MIT or Apache-2.0.

Download stable version

Download latest type (unstable)

16-bit Windows (Windows 1.x, 2.x, 3.0, 3.1, etc.) on 64-bit Windows

An altered type of winevdm (a 16-bit Windows emulator), ported to 64-bit Windows.

• If you get an error that VCRUNTIME140.dll is missing, instal Microsoft Visual C++ Redistributable for Visual Studio 2017 (32-bit)
• Drag and autumn Win16 executable document to otvdm.exe or execute otvdmw.exe.

• Download or compile
• Run "install" shortcut or right-click on install.inf and choose "Install"
• You can execute Win16 binaries directly!

If the register is initialized by Windows Update, execute the complete procedure again.

You can uninstall it by operating uninstall.reg.

See otvdm.ini.

• Install Visual Studio 2017
• Edit PropertySheet.props
• Compile

git copy https://github.com/otya128/winevdm.git cd winevdm mkdir build cd build cmake .. make

This program contains the following items

• CPU Emulator
  • 64-bit Windows cannot modify LDT(NtSetInformationProcess(,ProcessLdtInformation,,) continually returns error)
• wine based Win16->Win32 conversion codes:

  BOOL16 WINAPI DestroyWindow16( HWND16 hwnd ) { return DestroyWindow( WIN_Handle32(hwnd) ); }

  Relay routines from 16-bit to 32-bit are autogenerated by convspec

  53 pascal -ret16 DestroyWindow(word) DestroyWindow16

• DOS emulation for Win16
• 16-bit <=> native HANDLE conversion
• Fix compatibility problems, fix compatibility problems

When 64-bit Windows detects a 16-bit installer, it has a scheme to commencement an substitute installer which is not 16-bit. This program uses it.

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winevdm.exe [--app-name app.exe] command line winevdm.exe CALC.EXE

It can additionally run DOS executables (DOS emulator-like). You can set the DOS type alongside the VDMDOSVER surroundings variable.

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On September 8, 2026, we were alerted by a safety researcher, Gadi Evron from Knostic, to a tenant isolation matter affecting the GLM-5.3 Workers AI example (@cf/zai-org/glm-5.3) starting on August 27, 2026. We have remediated the issue, and we restored the example on September 9. We have confirmed that no another example hosted on Workers AI was affected.

Between August 27 and September 9, a small figure of customers using the GLM 5.3 Workers AI example received responses that contained data from another customers' requests made to the identical example shortly before theirs. In total, 155 responses were affected, all to prompts longer than 681,280 tokens.

After excluding our own evaluation and the researcher's, the requests we identified are accordant in timing and quantity alongside average long-context use, not alongside person often exploiting the bug. We established no evidence of deliberate exploitation.

Today we are publishing this study alongside a article from SGLang, the open-source conclusion motor that we use, as a coordinated disclosure. This article describes the bug that caused this issue, our reply timeline, and the consequent hardening we have conducted on our manufacturing SGLang deployments. We additionally item our continued efforts alongside the upstream maintainers of SGLang to location this and akin bugs.

What happened

GLM is a content generation example from Z.ai that is built for agentic coding workflows; it is among the many models available through Workers AI. Gadi Evron submitted a study on September 8 through our HackerOne program stating that extremely lengthy prompts sent to @cf/zai-org/glm-5.3 on Workers AI sometimes returned content that belonged to another users' sessions. Cloudflare reproduced the matter the next dawn and took the example offline inside 40 minutes of declaring an incident. We afterward deployed a corrected configuration and a patched conclusion engine, and restored assistance the identical day.  

This particular study of cross-request KV cache visibility was the outcome of two overlapping issues. The archetypal was a misconfiguration of the open-source conclusion engine, SGLang, used by Cloudflare. We deployed GLM 5.3 alongside a 1,048,576-token environment window, but the KV cache pond on our prefill GPUs lone had area for 681,280 tokens. We were conscious of this mismatch before launch. We expected the motor to come back an error for any immediate it could not process, and our long-context tests returned prosperous responses, so we did not accept this to be an issue.

The second was a bug in how SGLang handled the resulting over-length requests in disaggregated prefill/decode mode. A immediate that did not fit the pond was internally marked as rejected and replaced alongside a one-token placeholder, but alternatively of aborting the petition and returning a client error, prefill generated the KV cache for the placeholder immediate and transferred it to the decode node. The decode node received a "transfer complete" communication and assumed that prefill overwrote the KV cache from former requests alongside the complete immediate for this request, whenever in fact it remaining the bulk of the former province intact. The decode node subsequently generated a reply during attending complete old cache pages that motionless held data from before requests. As a result, responses to prompts longer than 681,280 tokens could merge data from another users' GLM 5.3 sessions.

Immediate remediation

After disabling the model, we took the following actions to accurate this exposure before restoring community admission to it:

To fix our configuration, we made the pond clasp the complete context. We adjusted our GLM 5.3 configuration to equivalent our GLM 5.2 configuration, which freed adequate GPU recollection that the prefill KV pond now holds concerning 2.04 myriad tokens, approximately twice the 1,048,576-token environment length. A immediate that fits the environment opening now continually fits the pool, so the rejection way is never reached for a valid request. This solitary prevents the bug from being exercised.

To fix the engine, we additionally made the rejection way safe. Relying on pond size solitary is fragile: any alter to quantization, tensor parallelism, CUDA chart sizes, or a forthcoming example type changes the pond size in a way that's uncomplicated to miss. Furthermore, there may be another situations in which the prefill scheduler can refuse a petition and trigger the identical bug. To forestall this, we additionally patched the motor so that a petition carrying a pending rejection is former at the disaggregation admission queue, alongside its first error, before any bootstrap, allocation, or KV transfer happens. We projected this upstream as #38704, which additionally had the decode node verify that the figure of tokens transferred matches the figure it reserved. The fix was already on SGLang chief in #36988, merged September 6 for a connected nonaccomplishment way but not yet released. The maintainers additionally merged #38935, which rejects these requests earlier, following our approach.

We validated the two changes against the researcher's reproduction manuscript and our own canary-marker tests before restoring the model.

Background: how we assist GLM 5.3

Workers AI runs GLM 5.3 alongside SGLang in a disaggregated configuration: a pond of prefill GPUs processes the immediate and produces the model's KV cache for it, afterward hands that cache complete distant straightforward recollection admission (RDMA), via Mooncake, to a pond of decode GPUs that create the response. We have written concerning this architecture before in our posts on high-performance LLMs and smaller, faster, safer models.

The KV cache in GPU recollection is a shared pond of fixed-size pages. Each petition is assigned pages whenever it is admitted and releases them whenever it finishes. Pages are not currently zeroed between requests; the next petition that gets a leaf merely overwrites it. This method is average among conclusion engines and is necessary to accomplish the finest performance, but it method that the conclusion motor is liable for tracking ownership of these recollection pages. KV cache leaf ownership and life tracking has to agreement alongside not fair shared ownership inside a node (KV cache offload and token generation the two admission the recollection alongside distinct lifetimes), but additionally ownership that is technically shared alongside the distant prefill node (which holds a simultaneous mutable citation to the page).

Furthermore, the size of this pond is not a configuration value. SGLang sizes it at startup from any GPU recollection remains following loading the example weights. The model's advertised environment dimension is a distinct configuration value. Nothing forces the two to agree, and it is our job to inspect that they do.

Issue one: misconfiguration

On our GLM 5.3 prefill nodes alongside our configuration, the pond came to 681,280 tokens during the model's environment dimension is 1,048,576 tokens. The selected KV pond size is printed by SGLang in logs during startup and exposed as a metric. We were conscious of this size mismatch; however, during pre-launch evaluation we observed prosperous HTTP position codes and a streaming build alongside no error frames during benchmarking. We assumed that the motor was capable of gracefully handling the difference between the KV cache pond and requested environment opening via streaming or several another mechanism.

This was one crucial gap in our testing: we lone validated HTTP reply codes and motor achievement at lengthy contexts, without validating example output and coherence. We now inspect KV pond size against environment dimension directly, from the operating engine, before any disaggregated deployment alter ships, fairly than inferring it from reply codes.

Issue two: SGLang bug

In v0.5.21, the SGLang squad has now addressed this bug in how it was handling prompts longer than the KV pond size. Here’s what was happening before.

When a immediate arrived that was longer than the KV pond could hold, SGLang's scheduler rejected it. The rejection way (Req.set_finish_with_abort) recorded the error to be returned to the client and exchanged the immediate for a one-token placeholder so that the petition could drain through the normal lot machinery without allocating genuine memory. On a single-node deployment this worked: the placeholder ran, the lot finished, and the client received an error.

In disaggregated mode such as we operate, the admission queues on the two prefill and decode checked whether a petition was finished, not whether it was rejected. A rejected petition reported itself as live. So:

1. Bootstrap. The router told the decode node the immediate dimension (8 pages in the diagram). Decode reserved 8 KV pages for the request.
2. Prefill admission. The prefill admission queue admitted any petition anywhere req.finished() was False. A rejected petition stored its rejection in to_finish, not finished_reason, so finished() still returned False. The rejected petition was admitted.
3. Prefill onward pass. Rejection had already replaced the immediate alongside a one-token placeholder. Prefill ran a trivial onward continue on that one token.
4. KV transfer. Prefill sent one leaf of KV to decode and signaled that the transfer was complete.
5. Decode admission. Decode's admission queue had the identical finished() check, so it accepted the handoff.
6. Decode attention. Decode started generating and notice peruse all 8 reserved pages. Only leaf P0 had been written by this request. Pages P1 to P7 motionless held what before requests remaining in them: another users' prompts and partial generations.
7. Response. The client got HTTP 200 alternatively of HTTP 400. prompt_tokens matched the lengthy immediate and the answer was coherent. It arrived faster than an 8-page prefill should take, and it contained satisfied from another users' KV pages.

Every petition complete the threshold behaved this way. This bug had been reported to SGLang by another person as matter #30233 on July 6, 2026, which Cloudflare noticed during examining this incident. We had been flagging these types of issues alongside SGLang since v0.5.10 and this safety event sped up activity that Cloudflare had already started in collaboration alongside the SGLang team. The place for this matter was released in SGLang v0.5.20, alongside follow-ups in v0.5.21 and v0.5.22, and is accessible for all another operators reliant on SGLang to use. We extremely propose all operators update SGLang requirements to v0.5.22 or later. For additional particulars on particular PRs, peruse on below.

GLM 5.3 was the lone example affected by this bug. Every another disaggregated deployment on Workers AI has KV pools larger than its environment dimension on the two prefill and decode, so no petition could attain the rejection path. We have applied the motor fix and the hardening described below to all example we assist alongside SGLang, and our deployment checks necessitate them for all new deployment.

Timeline

All times UTC.

Ongoing hardening effort

This bug belongs to a category of KV cache memory-safety bugs that Cloudflare has been operating to location for multiple months. In all case, a leaf is freed, reused, or peruse during item alternatively motionless has a assertion on it. We see two average causes.

The archetypal is leaf life inside a sole node. Several tasks on a decode node can clasp the identical leaf at once: token generation, offload to presenter memory, the KV transfer receiver, and the abort and error paths. If one of them frees the leaf during another is motionless using it, a new petition can be stated that leaf during it is motionless being peruse or written. Our before fixes for decode offload (#24226, #30899) and twice publish on grammar errors (#30937) were of this kind.

The second is a competition throughout nodes. When a petition is aborted during prefill is motionless penning its KV cache to decode complete RDMA, the writes already sent cannot be recalled. If decode frees those pages correct away, those pages can be stated to another request, and the delayed writes district in that request's KV cache. Cloudflare has contributed to the attempt to fix this competition through projected fixes (#31357, #38961) and a reproduction manuscript (#32564). In the two cases the outcome is corrupted output, or one user's data in another user's response, alongside no error raised. The bug in this event was different: a rejected petition went through the handoff all time, fairly than by a timing accident. But it had the identical result: decode study pages this petition never wrote. None of these covered the nonaccomplishment in this event in which prefill rejected the request, and decode never learned of it.

Since the incident, we have:

• Extended deferred publish to clasp prefill's origin pages as well, and to shield all nonaccomplishment type, not fair aborts. We projected it upstream as #38961, which was superseded by #40645, #41402, and #41404.
• Deployed bounded Mooncake transfer batches (#32758, from the SGLang community), so no sole transfer call can outlast the publish timeout.
• Changed our burden balancer so a client disconnect no longer aborts a petition mid-transfer. It holds the prefill association until prefill finishes and the decode association until the archetypal token, afterward closes both.
• Added a inspect to all disaggregated deployment change: KV pond size measured against environment length, deferred publish on the two roles, transfer lot limits inner the publish timeout, and all required patches applied.

All of these changes were released to our disaggregated manufacturing deployments.

Working alongside the SGLang team

We met alongside the SGLang maintainers at RadixArk on September 10, the day following the incident. We accepted that RadixArk would examine the competition conditions we had reported, upstream the integrity-checking idea, and build reproduction tests for this category of bug.

The following have landed on SGLang chief to location this event and connected KV cache competition conditions, and all but one are included in v0.5.22. Operators operating disaggregated SGLang should update to v0.5.22 or backport these patches as needed. 

These follow-ups are motionless plant in progress:

• #39729 Additional cleanup for --disaggregation-enable-kv-checksum. Merged, awaiting release.
• Zero KV pages on release, outline proposal: #46435
• Extend KV checksum validation from the prefill-to-decode handoff to all decode stage (see Integrity by construction, below)
• Broader KV Canary and KV Checksum example and characteristic support

What we propose to another operators

If you run SGLang in disaggregated prefill/decode mode, we propose the following:

1. Measure your KV pond and difference it to your environment length, on all prefill and decode node, from the engine's startup log, following all alter to weights, quantization, parallelism, or recollection fraction. If the pond is smaller, lesser the environment dimension to fit. Do not depend on a proxy to enforce the limit, and do not assume the motor volition refuse what does not fit. This is the inspect we did not enforce.
2. Upgrade to a build that includes #36988 and #38935 (v0.5.22 or later), so an over-length petition is rejected before handoff despite of pond size. This is the engine-side fix, and it is the backstop for the day your inspect in stage one is wrong.
3. Run alongside deferred KV publish on the two roles (default following #41023), and border your Mooncake transfer batches so a sole timed-out call cannot depart an unbounded rear of writes.
4. Turn on misconduct finding anywhere your example supports it. --disaggregation-enable-kv-checksum and --kv-canary do not yet shield all example architecture. Check your logs to verify requests are being verified. We are operating alongside the SGLang squad to broaden the two checks to additional architectures, including GLM 5.3, GLM 5.3 Flash, and DeepSeek V4.
5. Ensure your proxy is not generating unnecessary aborts. Configure your burden balancer to authorize requests to continue up to the archetypal token, so that you do not trigger an abort that requires cross-node coordination.

Longer term

Integrity by construction

Configuration checks and lifecycle fixes decrease the figure of ways a leaf can be misused. They do not create misuse impossible, and this event is a reminder that the next bug volition have a form we have not seen. The durable answer is for the motor to detect, at peruse time, that a petition is concerning to act complete a leaf it does not own, and neglect that one petition fairly than come back person else's data.

We built a prototype of this complete the summer and described it in August. Every bodily KV leaf carries a tag that changes whenever the leaf is reallocated, all petition records the tags it expects, and the mapping is checked before decode says the cache. In our tests it disbursal under 1% in throughput and rear latency. We tested it on GLM 5.2 in mid-August, but impaired it shortly afterwards because of reliability issues.

The prototype was additionally tied to particular notice backends and example architectures, so it could not be a broad fix. We are operating alongside the SGLang squad to build a manufacturing type into the motor that plant throughout all model, fairly than maintaining Cloudflare-specific patches. The per-transfer checksum in #39500 is a archetypal step: it validates the handoff from prefill to decode. Page-level tags go further, since they inspect all read, including says lengthy following the transfer.

Integrity by mathematical impossibility

RDMA copies data immediately from one node’s recollection to another’s. It’s critical for the two machines to concur on anywhere the data goes, how ample it is, and whenever it’s finished; any mismatch can logic the receiver to peruse the incorrect data. This bug is an example of a consistency nonaccomplishment in a distributed system, in which the two machines had inconsistent views of the province of that system.

Elsewhere in Cloudflare, we have put attempt into proving the safety of distributed systems using ceremonial verification methods. Topaz enables conflict-free IP choice for our authoritative DNS servers. Models of Meerkat’s QuePaxa implementation have demonstrated consistency bugs that were subsequently fixed.

We are now directing our cognition in ceremonial methods to KV cache transfer engines. This volition commencement alongside modeling and proving the safety of an RDMA-based KV cache transfer protocol. The goal is to example our manufacturing SGLang’s distributed KV cache scheme using Mooncake, afterward track and fix any divergence from the safety properties we desire to guarantee. The aim is to demonstrate — as far as imaginable — that a bug akin this cannot happen again. Realistically, we anticipate gaps in what the provers can inform us, but proving the remainder of the scheme allows us to additional closely assessment and detect those gaps.

Coordinated disclosure

We are publishing this study as part of a coordinated disclosure. We desire to appreciate the researcher, Gadi Evron, who reported this through our HackerOne program alongside a clear, reproducible manuscript and gave us period to fix it and to coordinate alongside the SGLang maintainers before publishing. We additionally desire to appreciate the SGLang squad at RadixArk, who treated this as their issue as fine as ours and landed the upstream fixes alongside urgency.

Read additional at the article from the SGLang team.

If you discover a safety matter in Workers AI, delight study it at hackerone.com/cloudflare.

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