Загрузка видео...

Не удалось загрузить видео

На главную

MLX + TurboQuant = Local Super Power Take a local (private) document(s) or codebase, pre-fill the 256k KV cache (the context) with the document(s) and system prompt, quantise and run on Apple's MLX and you have almost instantaneous, lossless document queries with total privacy. For a 75-page PDF (some...

jtdavies's profile picture

John T Davies 🇪🇺

2,954 subscribers

85,589 просмотров • 2 месяцев назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

You asked for document uploads. We answered with document uploads. This is so much more than a link or a screenshot, Webb actually analyzes the document and adds it to the CONTEXT of the canvas. A serious game changer. Ian Carroll
1:11

You asked for document uploads. We answered with document uploads. This is so much more than a link or a screenshot, Webb actually analyzes the document and adds it to the CONTEXT of the canvas. A serious game changer. Ian Carroll

The Webb

42,001 просмотров • 3 месяцев назад

This is the future of web design. I generated a website in seconds from a PDF document (with drawings, text, and images). No input from my part. No code. No work whatsoever other than uploading the document. This is incredible.
1:12

This is the future of web design. I generated a website in seconds from a PDF document (with drawings, text, and images). No input from my part. No code. No work whatsoever other than uploading the document. This is incredible.

Santiago

103,690 просмотров • 1 год назад

A small project I'm publishing for Neovim people: 🐠 butterfish.nvim This is a Neovim plugin to use language models for very fluent code editing. It veers away from the chatbox or codebase Q/A approaches and instead focuses on specific local edits, for example, select and rewrite a block of code. Do powerful things with few keypresses, without leaving the Neovim buffer!
4:56

A small project I'm publishing for Neovim people: 🐠 butterfish.nvim This is a Neovim plugin to use language models for very fluent code editing. It veers away from the chatbox or codebase Q/A approaches and instead focuses on specific local edits, for example, select and rewrite a block of code. Do powerful things with few keypresses, without leaving the Neovim buffer!

Peter Bakkum

29,069 просмотров • 2 лет назад

President of Ukraine: We did indeed have a positive meeting with President Trump and his team. First of all, to be honest, I raised the issue of air defense. And I think that the US will return with a positive result. I will definitely talk about it when the result is in Ukraine. Second, our security guarantees. They are indeed ready, and the agreement is ready for signing. Of course, there will be many different technical details. And based on this document, additional documents will appear, but the main, core agreement on security guarantees is in place. Now I am waiting for President Trump to set the date and place. It's up to him. We are ready to sign such an important [document] for us. I emphasize once again that I am confident this is a historic document. Why did I mention the many details? You know that such a document awaits ratification by the US Congress and the Ukrainian Parliament. Therefore, there are still many steps ahead on this path.
1:09

President of Ukraine: We did indeed have a positive meeting with President Trump and his team. First of all, to be honest, I raised the issue of air defense. And I think that the US will return with a positive result. I will definitely talk about it when the result is in Ukraine. Second, our security guarantees. They are indeed ready, and the agreement is ready for signing. Of course, there will be many different technical details. And based on this document, additional documents will appear, but the main, core agreement on security guarantees is in place. Now I am waiting for President Trump to set the date and place. It's up to him. We are ready to sign such an important [document] for us. I emphasize once again that I am confident this is a historic document. Why did I mention the many details? You know that such a document awaits ratification by the US Congress and the Ukrainian Parliament. Therefore, there are still many steps ahead on this path.

Anton Gerashchenko

27,842 просмотров • 5 месяцев назад

The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.
2:14

The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.

Aaron Levie

24,618 просмотров • 2 месяцев назад

As frontier models (e.g. Fable 5) continue to push the task horizon of knowledge work automation, it becomes ever more important for humans to be able to audit decisions back to the source context. It is extremely easy for agents to cite an entire document or document page, but much harder for them to trace back to the exact numbers/words/figures within a page. Today we've launched granular bounding boxes within LlamaParse, which allows you to obtain visual citations of every single word in the document. This allows human users to audit exact words and figures - not just general document regions or entire pages! Come check it out:
0:51

As frontier models (e.g. Fable 5) continue to push the task horizon of knowledge work automation, it becomes ever more important for humans to be able to audit decisions back to the source context. It is extremely easy for agents to cite an entire document or document page, but much harder for them to trace back to the exact numbers/words/figures within a page. Today we've launched granular bounding boxes within LlamaParse, which allows you to obtain visual citations of every single word in the document. This allows human users to audit exact words and figures - not just general document regions or entire pages! Come check it out:

Jerry Liu

23,071 просмотров • 19 дней назад

The 20 points for ending the war form a fundamental document. We are actively working on the key steps – they must be doable. From this fundamental document, we are developing at least two additional ones. The first is on security – regarding security guarantees with the United States. The second is on the economy, covering reconstruction and joint investment. Today, we focused specifically on the economic document. The principles of the economic document are absolutely clear, and we are fully aligned with the American side on this. Thank you. An economic action plan will follow. Europe will also be involved in reconstruction. In the near future, we plan to move forward on the other two documents as well. Rustem Umerov will continue the discussions.
4:34

The 20 points for ending the war form a fundamental document. We are actively working on the key steps – they must be doable. From this fundamental document, we are developing at least two additional ones. The first is on security – regarding security guarantees with the United States. The second is on the economy, covering reconstruction and joint investment. Today, we focused specifically on the economic document. The principles of the economic document are absolutely clear, and we are fully aligned with the American side on this. Thank you. An economic action plan will follow. Europe will also be involved in reconstruction. In the near future, we plan to move forward on the other two documents as well. Rustem Umerov will continue the discussions.

Volodymyr Zelenskyy / Володимир Зеленський

470,195 просмотров • 6 месяцев назад

Announcing a significant upgrade to Agentic Document Extraction! LandingAI's new DPT (Document Pre-trained Transformer) accurately extracts even from complex docs. For example, from large, complex tables, which is important for many finance and healthcare applications. And a new SDK makes using it require only 3 simple lines of code. Please see the video for technical details. I hope this unlocks a lot of value from the "dark data" currently stuck in PDF files, and that you'll build something cool with this!
3:25

Announcing a significant upgrade to Agentic Document Extraction! LandingAI's new DPT (Document Pre-trained Transformer) accurately extracts even from complex docs. For example, from large, complex tables, which is important for many finance and healthcare applications. And a new SDK makes using it require only 3 simple lines of code. Please see the video for technical details. I hope this unlocks a lot of value from the "dark data" currently stuck in PDF files, and that you'll build something cool with this!

Andrew Ng

299,092 просмотров • 9 месяцев назад

Using LLaMA 3 70B on Groq Inc to instantly refactor and document code. The implications for software engineering are wild. Gone are the days of waiting on an LLM for suggestions or code changes. Now, it's an instant feedback loop. Demo link in the comments:
0:21

Using LLaMA 3 70B on Groq Inc to instantly refactor and document code. The implications for software engineering are wild. Gone are the days of waiting on an LLM for suggestions or code changes. Now, it's an instant feedback loop. Demo link in the comments:

Matt Shumer

442,655 просмотров • 2 лет назад

IN NEWS: Extend raises a $17M round. "More documents will be processed in the next 6 months than all of history combined since the PDF was invented." - Kushal Byatnal "The global economy runs off of PDF files." "With LLMs, the capabilities are so much higher (for document processing and today's demand for document processing is through the roof.
1:17

IN NEWS: Extend raises a $17M round. "More documents will be processed in the next 6 months than all of history combined since the PDF was invented." - Kushal Byatnal "The global economy runs off of PDF files." "With LLMs, the capabilities are so much higher (for document processing and today's demand for document processing is through the roof.

TBPN

34,026 просмотров • 1 год назад

This Chinese developer runs 9 agents on Claude Code under a GPT-5.5 orchestrator and they close 500 client tasks a month without a single assistant. His client work is closed without him, on a single laptop and only three subscriptions. The entire system lives on one MacBook Pro M4 with 128 GB of memory and subscriptions to Claude Code and GPT-5.5 cost him approximately $300 a month. There is no CRM, no team, no office only a terminal window with 9 parallel streams. The orchestrator works with a simple system prompt: «You are the orchestrator of a client inbox. Classify every incoming email into 4 categories: code, content, analysis, communication. Delegate to the corresponding worker agent. When the result is ready, check it for completeness, send it to the client on my behalf, and mark the task as closed. Do not ask clarifying questions.» And the orchestrator checks the inbox every 30 seconds, classifies fresh emails, and distributes them to 9 worker agents on Claude Code, each of whom is responsible for their own class of tasks. Here is an example of how one of them closes a request to refactor a client's auth module: Task: refactor user-auth module Broke the monolith into 3 files by responsibilities Added unit tests, coverage increased to 87% Renamed 4 functions to camelCase according to the style guide PR is ready for review, link below» And so about 50 cycles a day. By noon 25 tasks are closed, by dinner 50, and by the end of the month 500. On average, it takes about 7 minutes from the appearance of an email in the inbox to sending the result to the client. This is more than what a live team of 6 developers, copywriters and analysts working 8 hours a day closes. This is no longer an agency. This is a workstation where an orchestrator replaces a manager, and 9 worker agents replace the staff. The pipeline goes from inbox to closing 500 times a month without human participation at any step.
1:32

This Chinese developer runs 9 agents on Claude Code under a GPT-5.5 orchestrator and they close 500 client tasks a month without a single assistant. His client work is closed without him, on a single laptop and only three subscriptions. The entire system lives on one MacBook Pro M4 with 128 GB of memory and subscriptions to Claude Code and GPT-5.5 cost him approximately $300 a month. There is no CRM, no team, no office only a terminal window with 9 parallel streams. The orchestrator works with a simple system prompt: «You are the orchestrator of a client inbox. Classify every incoming email into 4 categories: code, content, analysis, communication. Delegate to the corresponding worker agent. When the result is ready, check it for completeness, send it to the client on my behalf, and mark the task as closed. Do not ask clarifying questions.» And the orchestrator checks the inbox every 30 seconds, classifies fresh emails, and distributes them to 9 worker agents on Claude Code, each of whom is responsible for their own class of tasks. Here is an example of how one of them closes a request to refactor a client's auth module: Task: refactor user-auth module Broke the monolith into 3 files by responsibilities Added unit tests, coverage increased to 87% Renamed 4 functions to camelCase according to the style guide PR is ready for review, link below» And so about 50 cycles a day. By noon 25 tasks are closed, by dinner 50, and by the end of the month 500. On average, it takes about 7 minutes from the appearance of an email in the inbox to sending the result to the client. This is more than what a live team of 6 developers, copywriters and analysts working 8 hours a day closes. This is no longer an agency. This is a workstation where an orchestrator replaces a manager, and 9 worker agents replace the staff. The pipeline goes from inbox to closing 500 times a month without human participation at any step.

Blaze

29,917 просмотров • 1 месяц назад

Alex Finn reveals that you need to spend $20,000 to get the equivalent of Opus 4.6 on a local model "The advantages are that you aren’t paying for tokens, all you have to pay for is electricity.” "The fact you're not paying for tokens means you can run your model 24/7 365, you can't do that with Opus" “If you run it locally it’s a completely private system. Nothing you say is private if you're not on a local model”
1:36

Alex Finn reveals that you need to spend $20,000 to get the equivalent of Opus 4.6 on a local model "The advantages are that you aren’t paying for tokens, all you have to pay for is electricity.” "The fact you're not paying for tokens means you can run your model 24/7 365, you can't do that with Opus" “If you run it locally it’s a completely private system. Nothing you say is private if you're not on a local model”

Mikli

85,005 просмотров • 4 месяцев назад

Someone just open sourced a tool that honestly feels unfair for developers. you join a new company. 200,000+ lines of code. zero docs. zero context. normally you’d spend weeks just figuring out “what the hell is going on here”. now? you run ONE command. and the entire codebase just… lights up. → every file, function, class, dependency automatically mapped → full system-level understanding in minutes → interactive graph instead of boring static docs → ask “where is auth handled?” and it traces the entire flow end-to-end → shows ripple effects before you even make a commit → auto-builds architecture layers: API / Service / Data / UI → works with Claude Code, Codex, Copilot, Gemini CLI, everything → fully open source and free this is not “better documentation”. this is your codebase getting a brain. onboarding isn’t slow anymore. it’s instant.
1:55

Someone just open sourced a tool that honestly feels unfair for developers. you join a new company. 200,000+ lines of code. zero docs. zero context. normally you’d spend weeks just figuring out “what the hell is going on here”. now? you run ONE command. and the entire codebase just… lights up. → every file, function, class, dependency automatically mapped → full system-level understanding in minutes → interactive graph instead of boring static docs → ask “where is auth handled?” and it traces the entire flow end-to-end → shows ripple effects before you even make a commit → auto-builds architecture layers: API / Service / Data / UI → works with Claude Code, Codex, Copilot, Gemini CLI, everything → fully open source and free this is not “better documentation”. this is your codebase getting a brain. onboarding isn’t slow anymore. it’s instant.

divyansh tiwari

39,110 просмотров • 1 месяц назад

Claude Code can now work with 50+ AI models. Zen MCP connects Claude Code with Gemini, OpenAI, Grok, and local models using Ollama for the best analysis on each task. 100% Opensource.
0:39

Claude Code can now work with 50+ AI models. Zen MCP connects Claude Code with Gemini, OpenAI, Grok, and local models using Ollama for the best analysis on each task. 100% Opensource.

Shubham Saboo

39,748 просмотров • 8 месяцев назад

✨ I spent the last 48 hours making GPT-4 read the entire Solana validator codebase and write documentation, so doesn't have to. Introducing — an AI-powered chatbot trained on nothing but code that can answer deep technical questions. How it works 👇 But first... A huge shoutout to , Zahid Khawaja, and Sean. Their hard work made prototyping this thing a breeze. Without further ado... Devs like to write code, not documentation. Tribal knowledge is lost when devs move on to other projects, leaving future devs to sort through mountains of code and figure out not just how it works, but why it works that way. This is all about to change. GPT-4's ability to write code is stunning. It seems to understand something fundamental about writing software that previous models just didn't. This comprehension of the principles that drive the design behind a complex system carries over into its ability to document existing codebases in a truly impressive way. With the enlarged context window(s), it's now feasible to feed GPT-4 entire files of code and ask it to write documentation about how the code works. Taking this as a starting point, the process looks something like this: 1. Download repo. 2. Depth-first traversal of repo contents, ignoring things like package-lock and binary files. 3. For each file, feed to GPT-4 and ask it to write documentation in markdown. 4. Save the output in a separate location as [outputRoot]/[inputFilepath][inputFilename].md 5. For each folder, we ask GPT-4 to write a summary of the folder, taking the newly generated documentation for all files in the folder and the summaries from each of its subfolders as context. Write this to the filesystem as markdown. Now we have a filesystem that matches the structure of the input repo, but all files in the tree are markdown documentation of the corresponding code file. From here, we: 1. Load markdown documents into LangChain. 2. Embed all documents via OpenAI embeddings. 3. Store embeddings in Pinecone. When a user sends a query: 1. Embed query. 2. Find k-nearest markdown files. 3. Feed to GPT-4 with a prompt asking to answer the query based on k-nearest markdown documents provided. The craziest part of all this? GPT-4 actually wrote ~30% of the code. The results are pretty good for 2 days of work. There is certainly room for improvement. Some items that are top of mind: 1. TolyGPT will occasionally hallucinate answers. It is especially bad with links to external sources, like GitHub. The base model seems to know a bit about Solana already, and sometimes this creeps in. Fine-tuning the prompt can solve some of this. 2. Context selection is difficult in a codebase this large. For example, sometimes it will pull in details about the Solana SDK when asked about transaction processing. The SDK files can seem relevant depending on the phrasing of the question. It may be worth breaking the documentation into subsystems to limit this. 3. Not all files fit into the 32k token window. As of now, there are 23 (out of ~1,100) files that cannot be documented in their entirety. Some of these files are very important to how Solana works. Final thoughts: 1. GPT-4 is super powerful, and we're going to see a ton of tools that supercharge the entire software development lifecycle. This is not 12 months away. For the people that can afford it, these tools are here now. And they're only getting better. Act accordingly. 2. The price of inference has to come down for this to go mainstream. I spent about $300 prototyping this project, and the final crawl cost about the same. The high cost of GPT-4 will push developers to other, cheaper alternatives with similar performance. This is coming very soon. If you have a large software project and you're interested in something like this for your codebase, fill out this form and we'll be in touch this week. Or just DM me :)
0:51

✨ I spent the last 48 hours making GPT-4 read the entire Solana validator codebase and write documentation, so doesn't have to. Introducing — an AI-powered chatbot trained on nothing but code that can answer deep technical questions. How it works 👇 But first... A huge shoutout to , Zahid Khawaja, and Sean. Their hard work made prototyping this thing a breeze. Without further ado... Devs like to write code, not documentation. Tribal knowledge is lost when devs move on to other projects, leaving future devs to sort through mountains of code and figure out not just how it works, but why it works that way. This is all about to change. GPT-4's ability to write code is stunning. It seems to understand something fundamental about writing software that previous models just didn't. This comprehension of the principles that drive the design behind a complex system carries over into its ability to document existing codebases in a truly impressive way. With the enlarged context window(s), it's now feasible to feed GPT-4 entire files of code and ask it to write documentation about how the code works. Taking this as a starting point, the process looks something like this: 1. Download repo. 2. Depth-first traversal of repo contents, ignoring things like package-lock and binary files. 3. For each file, feed to GPT-4 and ask it to write documentation in markdown. 4. Save the output in a separate location as [outputRoot]/[inputFilepath][inputFilename].md 5. For each folder, we ask GPT-4 to write a summary of the folder, taking the newly generated documentation for all files in the folder and the summaries from each of its subfolders as context. Write this to the filesystem as markdown. Now we have a filesystem that matches the structure of the input repo, but all files in the tree are markdown documentation of the corresponding code file. From here, we: 1. Load markdown documents into LangChain. 2. Embed all documents via OpenAI embeddings. 3. Store embeddings in Pinecone. When a user sends a query: 1. Embed query. 2. Find k-nearest markdown files. 3. Feed to GPT-4 with a prompt asking to answer the query based on k-nearest markdown documents provided. The craziest part of all this? GPT-4 actually wrote ~30% of the code. The results are pretty good for 2 days of work. There is certainly room for improvement. Some items that are top of mind: 1. TolyGPT will occasionally hallucinate answers. It is especially bad with links to external sources, like GitHub. The base model seems to know a bit about Solana already, and sometimes this creeps in. Fine-tuning the prompt can solve some of this. 2. Context selection is difficult in a codebase this large. For example, sometimes it will pull in details about the Solana SDK when asked about transaction processing. The SDK files can seem relevant depending on the phrasing of the question. It may be worth breaking the documentation into subsystems to limit this. 3. Not all files fit into the 32k token window. As of now, there are 23 (out of ~1,100) files that cannot be documented in their entirety. Some of these files are very important to how Solana works. Final thoughts: 1. GPT-4 is super powerful, and we're going to see a ton of tools that supercharge the entire software development lifecycle. This is not 12 months away. For the people that can afford it, these tools are here now. And they're only getting better. Act accordingly. 2. The price of inference has to come down for this to go mainstream. I spent about $300 prototyping this project, and the final crawl cost about the same. The high cost of GPT-4 will push developers to other, cheaper alternatives with similar performance. This is coming very soon. If you have a large software project and you're interested in something like this for your codebase, fill out this form and we'll be in touch this week. Or just DM me :)

Sam Hogan 🇺🇸

374,577 просмотров • 3 лет назад

Head of Engineering for Claude Code at Anthropic. Fiona Fung: "After a while, it could start being a lonely experience - we were all just working with our agents so much." Claude now writes 80% of Anthropic's code. The team ships 8x more. An engineer, in Anthropic's own report: "On the days everything works, I can't help but think nothing I do matters." She names the fix in the episode. The dream was less typing - is this what you wanted?
28:47

Head of Engineering for Claude Code at Anthropic. Fiona Fung: "After a while, it could start being a lonely experience - we were all just working with our agents so much." Claude now writes 80% of Anthropic's code. The team ships 8x more. An engineer, in Anthropic's own report: "On the days everything works, I can't help but think nothing I do matters." She names the fix in the episode. The dream was less typing - is this what you wanted?

shmidt

33,041 просмотров • 4 дней назад

🚨| Sir Lewis Hamilton on these past weeks between races and what he has been working on with the team in the background, including development for 2026. "I've sent documents, so I've done through the year. After the first few races, I did a full document for the team. Then during this break, I had another two documents that I sent in, and so then I come in and want to address those. Some of its structural adjustments that we need to make as a team in order to get better and all the areas that we want to improve. Then the other one was really about the car, the current issues that I have with this car, some things that you do want to take on to the next year's car and some that you need to work on changing for next year." "We did development for, tried the 2026 car for the first time and started work on that... so big, big push, and otherwise just training." - F1 2025 Spa -
1:56

🚨| Sir Lewis Hamilton on these past weeks between races and what he has been working on with the team in the background, including development for 2026. "I've sent documents, so I've done through the year. After the first few races, I did a full document for the team. Then during this break, I had another two documents that I sent in, and so then I come in and want to address those. Some of its structural adjustments that we need to make as a team in order to get better and all the areas that we want to improve. Then the other one was really about the car, the current issues that I have with this car, some things that you do want to take on to the next year's car and some that you need to work on changing for next year." "We did development for, tried the 2026 car for the first time and started work on that... so big, big push, and otherwise just training." - F1 2025 Spa -

sim

74,210 просмотров • 11 месяцев назад

Good morning from Washington. Time for a quick tour. You know what you won’t find in my office? A desk. Or a chief of staff. We have an open office with all day interaction and collaboration between my employees. As for the chief of staff? I tried having a single point of failure before. Didn’t love it. Now our four directors report directly to me. Our office works faster, more efficient and better than it ever has under this set up. Everyone in the office has a voice and they are encouraged to use it. Our entire office is on the same page with the way we serve our constituents. And we love it. Now
2:27

Good morning from Washington. Time for a quick tour. You know what you won’t find in my office? A desk. Or a chief of staff. We have an open office with all day interaction and collaboration between my employees. As for the chief of staff? I tried having a single point of failure before. Didn’t love it. Now our four directors report directly to me. Our office works faster, more efficient and better than it ever has under this set up. Everyone in the office has a voice and they are encouraged to use it. Our entire office is on the same page with the way we serve our constituents. And we love it. Now

Rep. Nancy Mace

544,697 просмотров • 4 месяцев назад