Chris Kim

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Chris Kim

Chris Kim

@chriskim_dev

DevRel @Box

Katılım Eylül 2019
1.9K Takip Edilen5.1K Takipçiler
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Chris Kim
Chris Kim@chriskim_dev·
I built a @NousResearch Hermes workflow that my team at @Box uses to track AI trends. Every day at 7am, it researches what's happening across the AI ecosystem and generates a brief for our team. We use it to identify: • AI trends worth paying attention to • Open source projects gaining momentum • New tools developers are adopting • Content opportunities to work on next I've been building agentic workflows for months, and this is one I've actually integrated into my team's daily workflow. Here's how I built it:
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Chris Kim
Chris Kim@chriskim_dev·
I’ve mainly used AI for coding before I joined @Box Now I use AI for automating knowledge work. It’s exciting to inspire people like me to use AI for more knowledge work
Box@Box

We dropped a 30-page office lease into Box AI and asked @grok 4.5 one simple question: what would it actually cost us to walk away after three years? The lease is written to make you think the answer is easy: six months' rent (about $267K) stated right in the termination section. But the real cost is scattered in pieces across the whole document. The termination clause quietly points to a defined term that, when you chase it down, pulls in leftover build-out costs and broker fees from two other sections. The "free rent" you were given at signing is really taken back by a separate clause if you leave early. And there's a cleanup-and-restore obligation tucked into an exhibit at the very back. You only get the true number if you find all five and add them up — across 33 sections and 10 attachments. Grok 4.5 nailed it: ~$830K, about 3x the number the lease wants you to see, and it showed exactly where each piece came from. Then it kept going, pointing out the costs that have no cap, the auto-renewal trap that kicks in if you miss a deadline, and which terms to push back on before signing. That's the difference between skimming a contract and truly reading one and it's the kind of careful, connect-the-dots work over long documents that real business runs on. And this is the power of the latest reasoning models right now on working with unstructured enterprise data.

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Aaron Levie
Aaron Levie@levie·
The reason I have an unhealthy obsession with AI right now is because I've spent my entire professional life on essentially one problem: how do you increase the value of content in the enterprise. How do you secure it, how do you collaborate on it, how do you govern it, and how to integrate it across all your applications. But there's been one glaring issue that we've dealt with since the founding of Box. We could never really process information at scale in any real automated way. There have been many attempts at this problem (often in the search space), but nothing that really fundamentally transformed what you can do with enterprise knowledge. For years the primary kind of data that we could query, analyze, and process with computers was structured data. This meant anything you could shove into a database you could understand with computers - your CRM, ERP, product analytics, HR, and other data. But all of the unstructured data that powers our daily knowledge work - marketing assets, contracts, financial documents, medical research, engineering documentation - was only valuable when a human was operating on it. There was just simply no real way to apply automation at scale to any of this data, which meant all knowledge work was largely rate limited by our ability to process information ourselves, often manually. AI models have obviously dramatically changed this reality. And the past couple weeks perfectly highlight this incredible progress. GPT-5.6, Fable 5, Grok 4.5, Muse Spark 1.1, and a leading array of open weights models are all showing incredible advancements on working with unstructured data. The inherent broad intelligence, reasoning, math, and coding skills in these models, combined with deep domain expertise trained into them across finance, legal, healthcare, life sciences, and other critical fields, means that we're able to completely change what we can do with this unstructured data at scale. What this unlocks is the ability to ask insanely complex questions of your data that were never before possible, and let agents just run on for minutes or hours across these data sets to accelerate knowledge work. And it's not just about automating the work that we already do. While this is highly valuable, it wouldn't be particularly transformative. What's exciting is that you can now throw compute at unstructured data problems that wouldn't have been possible before. Analyze every risk on my contracts, do due diligence more deeply on a prospective investment or acquisition, look through all past client interactions in an industry to find best practices to replicate, comb through life sciences research or clinical trial data for new insights, and on and on. So that's why we're insanely excited about what AI Agents can now do with content on Box.
Box@Box

GPT-5.6 Sol is a breakthrough in complex reasoning and data analysis. Here, it analyzes hundreds of pages across a lending deal, reconciles terms across agreements, financials, diligence, collateral, and risk materials, flags issues, and saves a source-cited report to Box.

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Chris Kim
Chris Kim@chriskim_dev·
@gdb Please let chatgpt voice mode create documents and write code as well! I want to get some work done during my commute
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Greg Brockman
ChatGPT Voice is a real advance. We’ve always had to contort ourselves to our computers. Now feels like we’re entering a new era of a natural interface. Expect that we’ll soon spend much less time staring at screens, and regard today’s apps as painfully user-unfriendly.
Ethan Mollick@emollick

The new ChatGPT voice is quite impressive to use, really worth a minute to try it out on your phone. (while staying aware that the voice model is not going to be as smart as a full thinking model)

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Mahdin Choudhury
Mahdin Choudhury@mahdinchoudhury·
@SaadMalik I could try this, but our team shifted to markdown files that live in a repo, but kinda missing collaboration and sharing online. Honestly would go back to something like Google Docs but I need a desktop app
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Mahdin Choudhury
Mahdin Choudhury@mahdinchoudhury·
Notion is so messy and overwhelming that I literally never want to use it.
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Chris Kim
Chris Kim@chriskim_dev·
Trying out @WillowVoiceAI and I’m genuinely impressed. The Scribe feature is my favorite. I just tell Willow what I want to say, and it turns it into a polished post in seconds. This post was actually written using Scribe, which makes it even better. Really liking what I’m seeing so far. Also, having Privacy Mode selected by default during setup is such a thoughtful touch. It’s a small detail, but it shows that Willow puts user trust first from the very beginning, and that’s something I really appreciate. Little decisions like that make me like Willow even more.
Chris Kim tweet mediaChris Kim tweet media
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Chris Kim
Chris Kim@chriskim_dev·
.@ChatGPTapp Work is now the home for knowledge workers. Use the Box plugin to automate on your unstructured contents and 10x your productivity!
Box@Box

.@OpenAI just announced ChatGPT Work, and you can use these new features with the Box plugin today. The Box plugin, powered by the Box MCP server, is available in the new ChatGPT Plugin directory. That means that all of your workflows in ChatGPT Work - across Finance, Legal or Ops - can be grounded in your governed enterprise content in Box. In this demo, the agent reads a deal workspace, drafts the credit memo from the source documents, and saves it back to Box, where classification and approval routing take over.

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mick
mick@mickcodez·
I will also be pitting GPT 5.6 Sol against Grok 4.5, and I expect it to be quite a match. This is going to be the World Cup for agents.
Aaron Levie@levie

GPT-5.6 is now out. We've been evaluating the model family on the Box AI Complex Work eval, which tests the model with the Box AI Agent on a variety of extremely hard tasks using enterprise document sets. Sol is a big step up from GPT-5.5, especially on complex data-oriented tasks that require deep reasoning and analysis, and the gains concentrate exactly where enterprise work is hardest. Here are a few examples that we saw across our tests: * Financial Services (76% vs 71%): On a multi-year projection, Sol anchored to the correct opening balance sheet date rather than assuming a clean January 1 start, then carried revenue, earnings, and interest through to the right figures year over year where one early wrong assumption compounds through every downstream cell. * Healthcare (58% vs 46%): On a critical-care case review, Sol identified the correct diagnosis and intervention and avoided the dangerous misstep of ordering imaging before the time-critical procedure, a trap GPT-5.5 walked into. * Public Sector (74% vs 63%): Handed a class's raw gradebook and a new grading directive, Sol mapped each assignment to the right weight bucket, treating homework as zero-weight practice per the directive. It recomputed every student's grade to within a tenth of a percent, where GPT-5.5 drifted partway through. * Life Sciences (60% vs 51%): Across four separate compound datasets, Sol intersected the ranked target lists exactly (case-sensitive, no shortcuts) to find the biological targets common to all four, catching the shared targets GPT-5.5 missed. Sol reasons from the source definitions and checks the documents rather than taking them at face value and it's most reliable exactly where the numbers drive real decisions. This will be huge for enterprise agents using unstructured enterprise data. GPT-5.6 will be available to customers shortly within the Box AI Studio for building custom agents with.

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Chris Kim retweetledi
Aaron Levie
Aaron Levie@levie·
GPT-5.6 is now out. We've been evaluating the model family on the Box AI Complex Work eval, which tests the model with the Box AI Agent on a variety of extremely hard tasks using enterprise document sets. Sol is a big step up from GPT-5.5, especially on complex data-oriented tasks that require deep reasoning and analysis, and the gains concentrate exactly where enterprise work is hardest. Here are a few examples that we saw across our tests: * Financial Services (76% vs 71%): On a multi-year projection, Sol anchored to the correct opening balance sheet date rather than assuming a clean January 1 start, then carried revenue, earnings, and interest through to the right figures year over year where one early wrong assumption compounds through every downstream cell. * Healthcare (58% vs 46%): On a critical-care case review, Sol identified the correct diagnosis and intervention and avoided the dangerous misstep of ordering imaging before the time-critical procedure, a trap GPT-5.5 walked into. * Public Sector (74% vs 63%): Handed a class's raw gradebook and a new grading directive, Sol mapped each assignment to the right weight bucket, treating homework as zero-weight practice per the directive. It recomputed every student's grade to within a tenth of a percent, where GPT-5.5 drifted partway through. * Life Sciences (60% vs 51%): Across four separate compound datasets, Sol intersected the ranked target lists exactly (case-sensitive, no shortcuts) to find the biological targets common to all four, catching the shared targets GPT-5.5 missed. Sol reasons from the source definitions and checks the documents rather than taking them at face value and it's most reliable exactly where the numbers drive real decisions. This will be huge for enterprise agents using unstructured enterprise data. GPT-5.6 will be available to customers shortly within the Box AI Studio for building custom agents with.
Aaron Levie tweet media
OpenAI@OpenAI

Sol, Terra, and Luna, our GPT‑5.6 family of models, are starting to roll out now in ChatGPT, Codex, and the API.

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Box
Box@Box·
GPT-5.6 from @OpenAI is a three-tier model family designed to match the right capability to the right workflow. We evaluated models Sol, Terra, and Luna on enterprise document tasks: → Sol overall: 64.4% vs GPT 5.5's 63.3% → Healthcare: +12.7pp → Public Sector: +11.0pp → Life Sciences: +9.0pp → Data Analysis: +7.2pp Sol gets the hard, high-value work right. Terra and Luna bring near-flagship quality at 16-19% faster speeds for high-volume production workflows. Read the full evaluation here. 👇 blog.box.com/how-gpt-56-han…
Box tweet media
OpenAI@OpenAI

Sol, Terra, and Luna, our GPT‑5.6 family of models, are starting to roll out now in ChatGPT, Codex, and the API.

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Chris Kim
Chris Kim@chriskim_dev·
Bring agentic workflows & collaboration to your workplace. I'll go over how you can set this up with Box MCP & Box CLI. Sign up to the webinar if you are interested!👇
Carter Rabasa@crtr0

Want to learn how to build a company brain for your agents? 🧠 Join @chriskim_dev & @41five from the @box devrel team on July 28th at 4pm PT for our webinar on creating a governed memory layer with Box APIs and MCP. All code, no fluff, sign-up at: events.zoom.us/ev/AnJFdyO9sHq…

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Paul Klein IV
Paul Klein IV@pk_iv·
If I could only follow one person on X, it would be @levie, CEO of Box. We sat down to speak about why the REAL economy hasn't felt AI yet, the "model overhang" between capability and adoption, and what founders should actually build now that software is cheap. 00:00 - Cold open: agents use data like people do 01:53 - Is Redwood City the new AI epicenter? 05:18 - 90% of enterprise data is unstructured and was never automatable 07:46 - The model overhang: capability is outpacing adoption 10:35 - Why coding got the fastest AI takeoff 16:24 - Inside Box's agent-first rebuild 19:15 - Headless SaaS: agents will outnumber humans 100:1 25:41 - Model routing + why the applied layer wins, even bitter-lesson-pilled 27:45 - GTM is the new moat Checkout Navigators, our new pod on Youtube, Spotify, and Apple Podcasts!
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Chris Kim
Chris Kim@chriskim_dev·
The biggest takeaway from this podcast is how 90% of company content are unstructured data (pdfs, images, documents) and they couldn't be automated. @Box has changed this. Now you can: 1. Ask Box AI to extract metadata from +1000 files 2. Get json metadata of your unstructured content 3. automate This is one of the biggest unlock for enteprises imo.
Paul Klein IV@pk_iv

If I could only follow one person on X, it would be @levie, CEO of Box. We sat down to speak about why the REAL economy hasn't felt AI yet, the "model overhang" between capability and adoption, and what founders should actually build now that software is cheap. 00:00 - Cold open: agents use data like people do 01:53 - Is Redwood City the new AI epicenter? 05:18 - 90% of enterprise data is unstructured and was never automatable 07:46 - The model overhang: capability is outpacing adoption 10:35 - Why coding got the fastest AI takeoff 16:24 - Inside Box's agent-first rebuild 19:15 - Headless SaaS: agents will outnumber humans 100:1 25:41 - Model routing + why the applied layer wins, even bitter-lesson-pilled 27:45 - GTM is the new moat Checkout Navigators, our new pod on Youtube, Spotify, and Apple Podcasts!

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Chris Kim
Chris Kim@chriskim_dev·
We just got two Opus level coding models in the last two days with extremely competitive token pricing. This is why competition benefits the users!
Box@Box

We evaluated Muse Spark 1.1 from @AIatMeta Superintelligence Lab on Box’s Complex Work Eval. It was competitive with top-tier models, especially on structured data analysis and report drafting. The takeaway: models matter, but governed content is what makes enterprise AI useful at scale. Read more.👇 blog.box.com/muse-spark-11-…

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