Saw this Apple Senior MLE offer (shared with Chill Interview)

• 7 YOE
• Base: $235K
• Bonus: $23.5K
• Sign-on: $50K
• RSUs: $550K / 4 years
• Year 1 TC: $446K

Apple is in a weird spot for ML engineers right now.

It finally shipped the new Siri AI, but a big part of the intelligence layer relies on Google Gemini, and Apple has lost a number of senior AI people to Meta/OpenAI over the last year.

At the same time, Apple clearly isn’t giving up on AI. It’s still hiring heavily in ML, and just built its own model for China with Alibaba’s help.

So I’m curious how ML people view Apple now.

Is $446K + Apple stability/WLB worth it if you’re not working at the absolute frontier of AI? Or would joining Apple ML today feel like falling behind OpenAI/Anthropic/Google DeepMind?

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3 YOE for SWE

Both offers are in person for NYC

EliseAI offer is 270k base and 210k equity (840k over 4 years)

Google is 390k first year including base, equity, and bonus.

Which one makes more sense to take? The main concern is that Elise is not public and all the equity is paper money at the moment.

Saw this accepted Google Interaction Designer offer (shared with Chill Interview)

• NYC, 5 YOE
• Level: L4
• Base: $185K
• Bonus: $27.75K
• Sign-on: $20K
• RSUs: $500K / 4 years
• Year 1 TC: $422.75K

That number surprised me more than most SWE offers.

Especially because UX/design has felt like one of the shakier parts of tech lately. Google itself has cut UX and product-design research roles, and thousands of employees recently signed a petition asking for stronger layoff protections as the company pushes harder into AI.

At the same time, maybe AI actually makes the best interaction designers more valuable — somebody still has to figure out how humans are supposed to interact with agents, copilots, multimodal interfaces, and all these new AI products.

So is $423K for L4 design just an unusually strong offer, or are top UX/interaction designers becoming more valuable in the AI era, not less?

Google/design folks — what does the career outlook actually feel like internally?

Curious about more comp numbers of other companies, we've put up the data points at here

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A candidate with 6 YOE recently shared these two Menlo Park SWE offers with Chill Interview.

Snowflake

• $215K base
• $320K RSUs, vesting 40/30/20/10
• $32.25K annual bonus
• $375.25K Year 1 TC

Meta E4

• $210K base
• $30K signing bonus
• $350K RSUs, vesting 25/25/25/25
• $31.5K annual bonus
• $359K Year 1 TC

Snowflake is $16K ahead in Year 1, but the front-loaded vesting reverses the math later.

Assuming flat stock prices, recurring bonuses, and no refreshers:

• Meta 4-year: ~$1.346M
• Snowflake 4-year: ~$1.309M

By Year 4, Meta is paying roughly $50K more that year.

The company decision is more interesting.

Snowflake is a concentrated bet on enterprise data + AI infrastructure. Its latest reported quarter had 34% product-revenue growth, and management says products like Cortex Code and Snowflake Intelligence are benefiting from enterprise AI adoption. RPO grew to $9.21B, giving it a strong growth story if companies increasingly build agents on top of their own data.

Meta is the broader AI bet. Q2 revenue grew 28%, with 3.6B daily users across its apps, while the company continues pouring enormous amounts into AI infrastructure and superintelligence. The tradeoff is organizational volatility: Meta also recorded substantial severance costs from its May 2026 headcount reduction.

Career-wise, I’d frame it as:

Snowflake: deeper enterprise data, databases, distributed systems, AI infrastructure.
Meta: massive consumer scale, recommendation systems, ads, AI infra, and much broader internal mobility.

So would you take Snowflake for faster current growth and enterprise-AI upside, or Meta for steadier vesting, broader career optionality, and the slightly stronger 4-year package?

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A candidate with 6 YOE recently shared these two offers with Chill Interview.

Airbnb G8 — SF

• $205K base
• $20K signing
• $500K RSUs over 4 years
• $20.5K annual bonus
• $370.5K Year 1 TC

Anduril Senior SWE — Seattle

• $225K base
• $15K signing
• $450K equity over 4 years
• $22.5K annual bonus
• $375K Year 1 TC

The comp is basically a tie. Even over four years, Anduril is only about $33K ahead assuming flat equity values, recurring bonuses, and no refreshers.

The bigger difference is what that equity actually means.

Airbnb is public, so the RSUs are liquid as they vest. The business also looks healthy: Q2 revenue grew 17% YoY, Airbnb raised its full-year outlook, and it’s expanding beyond homes into hotels, services, experiences, and AI-powered travel.

Anduril is the much more aggressive upside bet. It raised $5B at a $61B valuation in May, but it’s still private, and Anduril explicitly says most shareholders can’t freely transfer shares without company consent. So the equity could appreciate significantly—but liquidity is much less certain.

Culture/WLB may be an even bigger separator. Airbnb offers Live and Work Anywhere, including working from home and living anywhere in your employed country without comp adjustment.

Anduril literally describes the job as “hard work, on hard problems, in hard mode” and emphasizes working in the office or field, speed, autonomy, and impact.

So would you pick Airbnb for liquid equity, flexibility, and a proven profitable platform, or Anduril for the Senior title, defense-tech growth, and potentially much bigger private-equity upside?

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After reading a lot of recent Meta SWE interview experiences, I’ve started to wonder whether Meta is actually one of the highest-ROI FAANG companies to prepare for.

Not necessarily the easiest interview. But maybe the most grindable.

What I mean is that Meta’s interview questions often seem relatively predictable compared with some other top companies.

For coding, a lot of the questions I’ve seen are recognizable LeetCode-style problems. They’re not always easy, especially once follow-ups and time pressure are added, but they usually don’t feel particularly obscure or dependent on some niche trick.

If you’ve done enough practice with common patterns — graphs, trees, intervals, BFS/DFS, heaps, hash maps, two pointers, etc. — there’s a decent chance you’ve seen something structurally similar before.

System design feels somewhat similar.

The questions are often built around fairly mainstream interview topics: feeds, messaging systems, storage, ranking/recommendation, rate limiting, large-scale APIs, and other systems that are already well covered by standard system design prep material.

Again, that doesn’t mean passing is easy.

You still need to solve coding problems quickly and cleanly, handle follow-ups without getting stuck, communicate while coding, make reasonable system design tradeoffs, and perform consistently across multiple rounds.

But the interesting distinction is that the preparation itself seems to transfer unusually well into the actual interview.

You can spend six weeks grinding common LeetCode patterns and mainstream system design, and a meaningful amount of that preparation may actually show up in your Meta loop.

That isn’t always true elsewhere.

Some companies have much higher interviewer variance. Others emphasize domain knowledge, practical coding, debugging, OOD, unusual system design prompts, or team-specific expertise.

And at some companies, getting through resume screening or team matching may be just as difficult as passing the interview itself.

So if someone told me:

“I have 6–8 weeks to prepare and my only goal is to maximize my probability of breaking into a FAANG-level company,”

I’m starting to think Meta might be one of the most rational companies to optimize for.

Not because the bar is low.

But because the bar is relatively legible.

You know roughly what game you’re playing, and you can get substantially better at that game through targeted preparation.

That makes me curious whether Meta might actually have one of the highest prep ROIs in Big Tech.

Does this match what people who interviewed there recently experienced?

Does Meta feel unusually predictable compared with Google, Amazon, Apple, Netflix, Databricks, Airbnb, etc.?

And if you had only two months to prepare for one Big Tech interview loop, which company would you choose purely based on prep ROI?

I’ve been noticing this pattern while going through recent Meta interview experiences on Chill Interview, so I’ve also been collecting the recurring coding and system design themes there. If you interviewed with Meta recently, would love to have you add your experience as another data point -> here

A candidate recently shared these two mid-level SWE offers with Chill Interview.

Microsoft 62 — Seattle

• $185K base
• $20K signing bonus
• $100K RSUs over 4 years
• $18.5K annual bonus
• $248.5K Year 1 TC

JPMorgan — NYC

• $180K base
• $30K annual bonus
• $210K Year 1 TC

Microsoft is $38.5K ahead in Year 1 and roughly $94K ahead over four years, assuming flat stock prices, recurring bonuses, and no refreshers.

Career-wise, Microsoft probably has the stronger optionality. FY26 revenue grew 18%, Azure passed $100B in annual revenue, and the company continues to spend aggressively across cloud and AI. That gives engineers paths across Azure, Copilot, developer tools, security, infra, and AI.

JPMorgan is a much bigger tech employer than people sometimes assume—it says it spends $18B+ annually on technology, with dedicated work in ML, AI agents, cloud, cybersecurity, and even frontier research. But the career signal is still more finance/enterprise-tech oriented than traditional Big Tech.

WLB is likely team-dependent on both sides, though Microsoft explicitly supports flexible work arrangements and flexible schedules.

So would you pick Microsoft for higher comp + broader tech career optionality, or JPMorgan for finance-domain depth and a more traditional enterprise environment?

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Jiahui Yu just announced that he’s leaving Meta to start a company, and the timing is pretty interesting.

He was one of the high-profile AI researchers Meta recruited from OpenAI and became a key figure around TBD Lab’s multimodal work. Public reporting also suggested Meta was offering extremely aggressive packages to recruit this tier of AI talent.

So seeing someone leave this quickly makes me wonder whether the bigger issue at Meta AI is no longer compensation, but the organization itself.

A few things I’m curious about:

1. If compensation is already extraordinary, what actually drives someone like this to leave? More research freedom? Faster execution? Ownership? Or simply much larger founder upside?
2. How stable is TBD’s direction internally? Meta’s AI org has gone through repeated restructurings and priority changes. For ambitious research teams, constantly shifting scope can matter more than compensation.
3. What does this environment look like for regular ICs? Top researchers can leave and start companies. Everyone else still has to deal with changing priorities, org reshuffles, scope competition, and projects that may suddenly lose sponsorship.
4. What exactly is the startup bet? If you already have one of the best-paid AI jobs in the industry and still choose to leave, you must believe the ownership/upside or ability to move faster is worth giving up a lot.

My current take is that Meta has clearly proven it can recruit elite AI talent with money.

The harder question may be whether it can create an environment where those people want to stay for five or ten years.

Money solves recruiting. It doesn’t automatically solve research freedom, organizational stability, ownership, or retention.

Anyone working around Meta AI / TBD have a different read on this?

I started a longer-running thread here to build a map of influential AI companies, what each one is actually building, and which layer of the AI stack they’re competing in: forum link

Interview Summary

The Anthropic technical screen focused on distributing a very large model checkpoint across a fleet of GPU workers as quickly as possible. I had prepared the peer-to-peer direction, but I spent too much time walking through intermediate approaches and trying to derive the optimal strategy. By the time I reached the full design, the discussion had lost momentum and there was limited time left for deeper exploration.

Interview Details

Technical Phone Screen — Fast Model Distribution Across GPU Workers:

The system design question asked me to distribute an approximately 500 GB model checkpoint from a central repository to a fleet of 100–1,000 GPU workers. Every worker needed to receive and verify the complete model before the new version could begin serving traffic.

The source repository had limited outbound bandwidth, while workers could transfer model data to one another. One notable constraint was that each worker’s downloads and uploads shared the same 10 Gbps network capacity.

• Distribution Strategies: The discussion began with simple direct downloads and then considered pipelined transfer, tree-based fanout, and chunked peer-to-peer distribution. The goal was to use aggregate cluster bandwidth rather than forcing every worker to download the entire model directly from the central repository.
• Chunking and Forwarding: The checkpoint could be divided into chunks so workers could begin forwarding data before receiving the complete model. The deployment system also needed to track chunk ownership and determine when each worker had received and verified the full checkpoint.
• Failure and Scale Requirements: The design needed to account for failed workers, slow network links, corrupted chunks, retrying transfers from alternative peers, and future expansion to approximately 10,000 workers.
• Interview Direction: I initially tried to demonstrate a gradual evolution from basic approaches toward peer-to-peer distribution. I spent significant time calculating and explaining several intermediate strategies, but some details in those suboptimal designs became unclear and the interviewer appeared to lose interest.
• Lower-Bound Discussion: My impression was that the interviewer cared less about finding one exact topology and more about whether I could establish a reasonable theoretical lower bound for the rollout time and defend the design relative to that bound.
• Time Management: I eventually moved directly to the peer-to-peer design and proactively covered the remaining reliability and operational considerations. However, there was limited time left, and the interviewer did not engage deeply with many of the follow-up areas I raised.

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Need thoughts on this offer I got after 7 years at Meta.

Base: 270K
Performance bonus: up to 10%
Equity: 240K over 5 years (1 year cliff)
No sign on bonus

Role: Senior Software Engineer
Is this a fair offer?

Curious which one you would choose have been at pal for a year now but still considered early career entry level.

Meta Production Engineering I heard is like SRE but they pay the most for sure with the stock. However, I don't want to be stuck doing SRE work. Bloomberg SWE seems like the best option because it's more general I can focus on what I like and my resume is SWE. Pal fdse has been fun but being and fdse makes it hard for me to do software engineering work and I kind of don't want to travel that much. not sure how switching into swe works for meta and palantir, but have seen some people in pal switch to swe. I signed meta and bloomberg already but will have to choose one soon. also seems like bloomberg and palantir are really flat structure so hard to grow, but not sure how much that matters in this economy where having a job matters more and meta more prone to layoff.

assume all locations are the same
current TC: 200k
meta TC: 200k
bloom TC: 200k

Saw this Google Staff MLE offer (shared with Chill Interview)

• PhD, 6 YOE
• Base: $285K
• Bonus: $57K
• Sign-on: $50K
• RSUs: $1.2M / 4 years
• Year 1 TC: $848K

What surprised me most is the combination of 6 YOE + L6 + nearly $850K TC.

For a traditional SWE, getting to L6 can take a long time. In ML right now, it feels like the right background can accelerate both leveling and comp pretty dramatically.

The catch is Google’s 38/32/20/10 vesting:

$848K → ~$726K → ~$582K → ~$462K

before refreshers.

So is this actually an ~$850K job, or more like a ~$600K job with a very strong first couple years?

Google / ML folks: are offers like this becoming normal for strong L6 MLE candidates, or is this still an outlier?

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Any Salesforce fde’s here? I am interviewing for the role and have been invited for an half hour onsite interview , so need some advice and prep help!!!

>Sharing a Netflix Staff SWE Interview Experience submitted to Chill Interview.

Interview Summary

The Netflix process stretched from an application in late March to an onsite in July and included a recruiter screen, hiring manager conversation, technical phone screen, and five onsite rounds split across two days. Because the role was on the Ads team, ad-tech experience came up repeatedly throughout the process, including ad booking and reporting, frequency capping, and product-specific behavioral questions.

The technical interviews felt positive overall, and several interviewers indicated that the conversations had gone well. About two weeks after the onsite, however, I was told that the team was moving forward with finalists who were considered a closer match.

Interview Details

Round 1 — Recruiter Screen: Netflix Culture and Background The recruiter screen lasted about 30 minutes and covered my background, motivation, and familiarity with Netflix's culture. A meaningful part of the conversation centered on how I interpreted Netflix's culture principles and whether that environment matched the way I preferred to work.

Round 2 — Hiring Manager: Ads Experience The hiring manager interview lasted roughly 45 minutes. Because the opening was on the Ads team, the interviewer spent a significant amount of time asking about my previous advertising-related experience. The recruiter had already emphasized that the team was looking for candidates with relevant domain exposure.

Round 3 — Technical Phone Screen: Coding + Production Follow-Ups The coding problem itself was relatively simple and took less than ten minutes. The remainder of the round shifted toward production engineering questions. The interviewer asked how I would think about production failures such as out-of-memory conditions, increasing load, and scaling the system. Other follow-ups covered partitioning and monitoring in a production environment.

Round 4 — Onsite Coding: Video Dependency Ordering The onsite coding round asked a dependency-ordering problem in the context of Netflix's video rendering pipeline. Videos or rendering jobs could depend on other pieces being completed first, and the task was to determine a valid processing order. The underlying structure was a topological-ordering problem. After coding, the interviewer asked about edge cases and production scenarios, similar to the discussion during the phone screen.

Round 5 — Data Modeling: Ad Booking, Delivery, and Reporting The data-modeling round used a real-world advertising workflow. The scenario involved a client that wanted to book an advertising campaign, have those ads delivered, and later view reporting about campaign performance. I was asked to define the main entities and relationships required to represent the campaign lifecycle. The model needed to support the progression from booking through delivery and reporting. Because I had previous ad-tech experience, this round felt relatively familiar.

Round 6 — System Design: Ad Frequency Capping The system design round focused on frequency capping: limiting how many times a particular ad can be shown to a user over a defined period. The interviewer was very senior and pushed on the design at a fairly deep level. Prevent excessive repetition of the same advertisement while maintaining a good user experience. The discussion covered how impression activity should be tracked and how the system should enforce caps at large scale.

Round 7 — Manager Behavioral The manager behavioral round contained fairly standard questions. I was asked about a project I was particularly proud of and a situation involving disagreement or conflict. Other questions covered operating in ambiguous situations and how I handled feedback.

Round 8 — Director Culture and Ads Discussion The final interview combined Netflix culture questions with another deep discussion of my advertising background. Roughly two-thirds of the conversation focused on ads-related experience, while the remainder covered my interpretation of Netflix's culture and a few standard behavioral questions. I found parts of the behavioral and culture conversations harder to follow than the technical rounds, but I tried to clarify and respond carefully throughout.

Outcome About two weeks after the onsite, I received a rejection stating that other finalists were a closer fit for what the team was looking for. No detailed interview feedback was provided, so I never got a clear signal on whether the decision came from technical performance, culture/behavioral fit, or simply stronger alignment from another candidate.

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Saw this accepted Google L3 offer (shared with Chill Interview)

• 0 YOE, Master’s
• Base: $165K
• Bonus: $24.75K
• Sign-on: $15K
• RSUs: $100K / 4 years
• Year 1 TC: $242.75K

Pretty wild contrast with how bad the new-grad market feels right now.

People keep saying junior SWE is getting squeezed by AI and companies want fewer entry-level engineers. But if you actually make it through the Google funnel, you’re still starting at nearly $250K.

The other interesting part is the RSU grant — only $100K total, and Google’s 38/32/20/10 vesting means the package gets noticeably weaker after the first couple years unless refreshers kick in.

For recent grads: is Google L3 still the dream outcome, or has the upside shifted toward AI startups / smaller companies where you can grow faster?

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Saw this Capital One Senior Lead SWE offer in McLean:

• 9 YOE
• Base: $265K
• Bonus: $26K
• Sign-on: $40K
• Equity: $35K
• Year 1 TC: $366.5K

It obviously doesn’t have the equity upside of Meta/Google, but $265K base in Virginia is pretty solid.

What makes Capital One interesting to me is the tradeoff. Employee feedback often describes the WLB as pretty reasonable, and the company is still hybrid rather than full-time RTO. But the other side of the culture seems to be fairly corporate: performance reviews, stack ranking and internal politics come up a lot too.

So maybe this is one of those jobs where you stop optimizing purely for TC.

For people who’ve worked there: is Capital One actually a good place to coast at Senior while still making $350K+, or is the “good WLB” reputation overstated?

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Had 4 rounds: Coding + Googliness screening were positive, with recruiter saying coding feedback was good and Googliness was very good.
Onsite R3 was a difficult graph problem—I initially got stuck, then derived the optimal approach and interviewer was satisfied, but ran short on time and had a few bugs remaining.
R4 was another graph problem where I quickly gave the optimal approach, coded it, handled a PQ optimization with a few hints, and gave correct time/space complexity.
What do you think my likely ratings are for each round, and overall chances of clearing?

Anyone from Apple moved to Nvidia in recent times. I just received an offer for ic4. I want to know how the work and culture at Nvidia is.

The recruiters are more emphasizing on stock growth and offering lower RSU and I feel like they already had their time. What do you guys think.

MY APPLE pay is as competent as nvidia but recruiter is adamant and not budging on negotiation. They are considering nvidia growth will be more than apple and the recruiter literally said Less politics compared to apple lol😂

Anyone help me with how Nvidia is and what’s the refreshers for ic4 look like.
Yoe: 9 years

Interview Summary

The DoorDash process started with a recruiter call, followed by a Code Craft screen and a four-round virtual onsite covering debugging, behavioral / hiring manager questions, system design, and AI-assisted coding. The overall process moved quickly and was well organized, with plenty of scheduling options for the onsite.

A recurring theme was that DoorDash seemed to care less about producing code line by line and more about whether I could reason about production behavior, failures, concurrency, scalability, and system boundaries. My weaker rounds were the debugging interview and the AI coding exercise, and I ultimately received a rejection.

Interview Details

Recruiter Screen — Background and Why DoorDash: The recruiter conversation was straightforward and mainly covered basic background information. There were no substantial behavioral questions in this round beyond standard motivation questions such as Why DoorDash? I heard back within a couple of days and moved on to the technical screen.

Code Craft — Simplified Dasher Pay: The technical screen used a simplified version of the recurring Dasher Pay problem. Unlike some reported versions, this one did not introduce additional coding requirements such as double-pay-rate windows. After completing the core implementation, the interviewer shifted into lighter system-design-style follow-ups.

• Production Failures: One follow-up asked what should happen if a downstream dependency became unavailable or failed during the workflow.
• Implementation Environment: There was no starter code. In Java, I had to create the surrounding Main class and my own way of invoking the implementation to validate the results. A few simple test cases were provided, and adding additional corner cases would likely have helped demonstrate robustness.

I passed this round and advanced to a four-round virtual onsite.

Virtual Onsite Round 1 — Debugging a Random Dasher Picker: The debugging round used a randomized variant of Dasher Picker. The provided implementation maintained an index-to-Dasher mapping. The system supported adding Dashers, removing them, and selecting a random Dasher. The bugs were not limited to basic collection logic.

• Correctness and Concurrency: In addition to fixing issues around maintaining valid indices after removals, the interviewer expected me to notice multi-threading concerns. The discussion included synchronization at the method versus block level and what can go wrong if a synchronized section makes a slow external API call and holds a lock during a timeout.
• Distributed Follow-Up: After the local implementation was fixed, the interviewer asked how this design would change in a distributed environment and what new challenges would appear.

This was one of my weaker rounds. I had prepared more heavily for other Dasher Picker variants and was less comfortable with the concurrency portion.

Virtual Onsite Round 2 — Hiring Manager and Behavioral: The hiring manager round contained only a few main behavioral questions, but each answer received substantial follow-up. The interviewer focused on situations where I proactively identified a problem or initiated a project rather than simply executing assigned work. Follow-ups explored the business impact of my work, how I measured that impact, and how I use AI in my engineering workflow. The interviewer was friendly and left a meaningful amount of time for candidate questions.

Virtual Onsite Round 3 — Project Deep Dive + Alert Notification System: The system design interview was split into two parts. The first part of the interview were spent discussing one of my previous projects. The interviewer asked architecture-oriented follow-ups, including what I would change if I were building the system again. The second portion asked me to design a simplified Alert Notification System. Unlike a consumer notification service, this system did not directly send notifications to end users. Instead, alerts were delivered to downstream services.

• Retry and Failure Handling: The interviewer went deeply into how retries would actually work rather than accepting a high-level answer such as placing failed messages into a retry queue.
• Scalability: The design also needed to handle failures and increasing load. The interview interface included separate areas for requirements / notes and architecture diagrams, so clearly capturing functional and non-functional requirements early in the round was useful.

The interviewer was collaborative and provided hints throughout the discussion.

Virtual Onsite Round 4 — AI Coding: Multi-Service Refund Workflow: The final round was an AI-assisted coding exercise centered on a refund workflow represented by a DAG. There was no starter code. I needed to build multiple components, including a service that retrieved order information, a service that accepted refund operations, and the workflow connecting them. The important requirement was that these were not merely mocked classes calling one another inside a single process.

• Real Local Services: The interviewer expected multiple services to actually run locally, expose HTTP endpoints on different ports, and communicate with one another through API calls.
• AI-Assisted Implementation: I initially interpreted the problem as a more traditional coding exercise where several classes could simulate the services locally. After realizing the interviewer expected real HTTP services, I had AI substantially restructure the implementation. That change came late enough that I did not have much time to inspect or validate the generated code carefully.

The emphasis seemed to be on whether I could get the services running and interacting end to end within the available time, rather than building a particularly sophisticated DAG execution engine.

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Saw this accepted Meta MLE offer (shared with Chill Interview)

• 5 YOE
• Base: $245K
• Bonus: $49K
• Sign-on: $85K
• RSUs: $1M / 4 years
• Year 1 TC: $629K

What jumped out to me is the level.

This is still E5 with only 5 YOE, but the equity grant alone is $1M. That’s starting to look more like the comp people used to associate with Staff-level engineers.

Feels like the gap between “regular SWE” and engineers with the right ML/AI background is getting wider really fast.

For Meta folks: is this becoming normal for E5 MLE hires, or is this an unusually strong offer?

And for traditional SWEs trying to move into AI — is the comp gap actually this big internally too?

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