AI is going to make remote teams and video calls less frustrating

While COVID changed the nature of work for lots of people, at SoftwareSeni online and remote work has always been a part of our experience.

Team extensions for software development is the key service we provide our partners. Regular virtual stand-ups, code reviews, as well as one-to-one meetings for things like discussing design decisions and troubleshooting are part of the normal working day for our teams.

As comfortable as we are with video meetings, we are quick to adopt any practices or tools that can help us streamline and get the most from them. So we’re looking forward to trying out the new AI powered features announced by Zoom and Google.

Let’s have a look at the features we think will make a real difference for everyone who relies on video meetings. Having said that, this quick overview is biased towards mixed local/remote software development teams.

Summarising Meetings in Real-Time

This one seems like it is targeted at meeting with multiple participants, but we think this will help everyone. First, being freed from taking notes so you can focus on what is being discussed is a big win. Second, having a thorough summary of even a one-on-one meeting means no-one has to worry about the quality of their note-taking skills or their memory.

We’re also assuming that these summaries end up being incorporated into, say, your Google Drive, if you are using Google Meet. Then they become searchable and discoverable and easy to incorporate into your planning and follow-ups.

Catching Up on Missed Meetings

This is an extension of the previous feature, but is worth pointing out. What we like about this is, again, documenting the meeting is handled by Google’s or Zoom’s AI. They’ll extract the major talking points and action items for anyone who missed it.

If that’s not enough detail, both services offer transcripts if you need the full text of the meeting.

Captioning and translation

Like watching Netflix with subtitles on, but more useful. Captions on meetings can really help with the inconsistent audio across multiple speakers.

Both Google Meet and Zoom also provide the option to deliver translated captions. We find that despite our multilingual team’s high level of language mastery, translated captions improve the clarity and quality of communications in video meetings.

The captioning and translation is not perfect, but like watching movies, the combination of speech and text reduces the effort and concentration needed to follow what is happening.

“Cosmetics” of video calls

This isn’t really a thing we care about, but maintaining privacy by having your background blurred out or replaced is an option we can get behind. We should all want video calls to be comfortable for everyone.

Duet for Google Meet is also promising “studio lighting” and other image enhancements, as is Zoom. This is starting to veer into the world of “filters” and how we appear on camera compared to “reality” (remember the lockdown boomers rendered as potatoes on call?). But again, if these features help team members feel more comfortable on call or even simply enhance everyone’s experience by providing clearer views of each other, then we’re all for it.

Other features we won’t be using

Zoom’s AI Companion is all about deepening integration with their suite of calendar and messaging tools. We’re a Google Workspace shop, so Zoom’s features like AI-powered meeting scheduling is not going to impact us. The nature of software development doesn’t really require it. You’re either participating in scheduled stand-ups, etc, or you’re hopping on a call you’ve just coordinated via chat.

Zoom also has features that monitor and report on conversations. They sell it as a feature to help improve the quality of your sales team’s calls. Not the kind of thing our developers need for their day-to-day interactions with other team members.

Will AI-powered video calls be a game-changer?

We’ll let you know, but we suspect that, despite the immense investment in tech and infrastructure these features represent, we’re only going to see an incremental improvement, more of a quality of life increase than an efficiency multiplier.

Calls will go more smoothly because captions and translations, as well as transcripts and summaries, will allow everyone to focus on the purpose of the call instead of the technology of the call. Those transcripts and summaries should reduce the flurry of follow-up emails and messages after calls, which will be welcome.

Once your business gets above a handful of people, communication becomes the key component in your success. Our headcount is north of 150 so we know this firsthand. In the balancing act that is making your business work, a balancing act that includes not just optimizing who you work with but where they work from, video calls are going to be part of your internal comms mix. These new features should help you bring them closer to the quality and effectiveness of in-person meetings.

But we’ll wait and see if it turns out that way.

WFH vs RTO: How and why we transitioned

As more and more businesses require employees to return to the office (RTO) there have been two popular, but cynical, takes on the trend. The first is that the businesses don’t want their offices sitting empty, especially if they own them rather than lease them. The second is that the businesses are control freaks and want to be able to once again monitor their employees every moment.

These takes are not completely wrong. There will always be businesses that make decisions based on dubious reasoning. But for most businesses that have more than a handful of employees, bringing them back to the office can be a matter of survival.

As a company that provides software development services and employs a large and growing team of software developers, we’ve felt the impact of Work From Home. It’s affected our staff and our operations to the point where we are now implementing practices to counteract it.

This article is a discussion about what we experienced during the COVID WFH period and its aftermath, what we’ve learned from it, and how we are changing the way we work to address it.

How Work From Home impacted our day-to-day operations

During the early part of WFH under COVID we experienced minimal impact in our transition away from the office. This was due to all of our staff transitioning directly from the in-office experience where they all knew each other well and were part of the culture.

It was during the later phase of COVID WFH, when everyone had undergone extended isolation and new staff members had joined us, that we started to see the impact on our culture and our mission.

The impact can be summarised in 4 words – Continuity, Clarity, Capability and Growth.

Continuity, in this context, includes maintaining a business’s culture, practices and institutional knowledge while also preserving staff development and advancement and integrating new hires into the business.

Clarity is having a clear view into the operations of the business. Some parts of this view can be found in reports. Other parts are found in meetings. Some parts are found only by in-person interactions and observations.

Capability is being able to execute at the level your business needs. The big challenge of running a business is you never have the perfect person for every role. You need to be able to work with the resources you have. But these resources might limit your options.

Growth is exactly what it is. It might be growth in product or service offerings or growth in size or growth in profitability.

The impact of Work From Home on staff

No-one can deny that people like working from home. At the same time, no-one can claim that everyone likes working from home or that everyone is better off working from home or that there are no negative consequences created by WFH strategies.

What we found with our large team was that the people who fared best under work from home were in senior roles, had strong communication skills, and were in positions that required only minimal collaboration or reporting. Senior developers are an example of this kind of role.

On the other hand, we found junior developers were struggling to complete work. This caused them stress and in some cases anxiety. They suffered, their work suffered, and more pressure was put on senior developers to fill in gaps. But it was during lockdown and we all just soldiered on.

When we began bringing employees back to the office we found that many of our junior developers were still just that – junior developers. Despite the amount of time they had been with SoftwareSeni they had made little advancement in their skillset, including not just programming but the soft skills that are part of working within a team, as well as in carving out their own unique career path. We saw a similar slowdown in our mid-tier developers as well.

We use a mix of formal and informal mentoring. Formal mentoring includes activities like shared planning sessions and code reviews. Informal mentoring covers all those little moments of learning and experience transfer that happen in an office. In an office environment it is easy to turn to someone more experienced for help when you get stuck. In an isolated environment like WFH, where finding a free coworker with the right experience and organising a video call and screen sharing is necessary to answer a question, that question might go unanswered, creating delays or resulting in a poorer quality solution.

There is also learning by osmosis that takes place in an office environment. Being present while more experienced coworkers discuss problem solving and strategies for the problems they face is itself an opportunity for learning.

You may be reading these examples and imagining solutions for each of them that don’t require working in an office. Just put a call out on Slack. Invite junior developers to Zoom calls. They look like solutions, but the level of friction in using them and the quality of the experience they provide results in worse results than simply being in the same space together. We know this because we had staff working from home for nearly two years using these “solutions” and things are going so much better now most of the team is back in the office.

Bringing the team into the office is necessary for Continuity: continuity of our team members’ careers, continuity of inhouse experience, and continuity of “institutional knowledge” and our culture.

Without this emphasis on maintaining continuity a business like ours with a team in the hundreds could see a collapse of our capabilities and our quality of execution drop to the point where we can no longer be competitive.

The impact of Work From Home on management

Goodhart’s law states “When a measure becomes a target, it ceases to be a good measure”. This law is about how when given a target, like a KPI, people’s natural tendency is to optimise their actions and find ways to game the system to hit the target.

During Work From Home we found something similar happening in our regular meetings. It wasn’t malicious, it was just the outcome of the team striving for efficiency.

Since, of course, all our meetings were video meetings, we adopted clear agendas for running our regular meetings. We all knew in advance what needed to be discussed and we got through them quickly and cleanly despite the inherent clumsiness of group video calls.

But once we returned to the office it became apparent that what was being reported in the meetings had become a streamlined, tightly focused delivery of the agenda items. Issues of all kinds had developed in the business but they had fallen through the cracks of our carefully planned and cleanly executed meetings.

The issues had been allowed to develop because as everyone was sitting alone in their home offices they weren’t just physically isolated from each other, they were physically isolated from the issues. And almost all the issues were complex and identifying them as issues from a single perspective was unlikely.

You can’t have a report for everything. You can’t have a KPI for every facet of your business. Workflows can’t cover every situation. But there are still countless factors that need to be monitored and dealt with.

They’re different for every business, they’re different for every department, and they’re different for every team. Seal everyone off from each other and you may never be able to connect the dots.

Clarity comes from having multiple views of the same shared experience. Clarity comes from secondary sources, back channels, passing comments, and interactions with the team. This is where recognising problems and solutions begins. From here they end up in reports.

Your team might not be ready for Work From Home

This is where Capability comes into play. Your business needs to be able to run, survive and thrive with the team you have available. What you can do is constrained by the skill and experience of your team.

We found performance under WFH was dependent on the skills of the manager. WFH, and remote work in general, requires a higher level of skill to pull off than normal in-office team management.

It takes time to adjust to the different management style. Again, like our meetings discussed above, reducing all interactions to video calls that need to be scheduled or coordinated changes outcomes.

Simple things that don’t need a meeting to be shared, like job status, hours worked, and so on, are easy to monitor. Addressing them, however, along with other more subtle concerns, like engagement, role clarity, skill acquisition, morale, and work quality, is made more difficult by the inherently confrontational format of a video call.

One of our main services is software development team extensions. Remote teams as a service, basically. You would expect that we would be masters of remote team management. But the teams are remote teams only for our clients. All of our team members, before COVID, worked out of our offices. Part of our service is acting as a secondary level of management or as a team support role for our clients. This was done in-person until the lockdowns. Which meant our team support staff had to learn the new skills required to manage remotely. Some adapted better than others.

How to grow if your Clarity, Continuity and Capability are impaired?

Some businesses rely on employees executing in roles with basic skill requirements. Other businesses rely on a small number of highly skilled employees who can execute independently of each other. These businesses can continue to do well under Work From Home or with a fully remote team.

If you’re like us, where your team is growing, where you need employees advancing between roles, and you need the resilience of a team where knowledge is shared instead of siloed, then Work From Home is not the best option.

Growth for a business relies on having a long term vision and the strategies that support it. For SoftwareSeni, our team is the heart of our strategy: growing our team, helping them develop their skills, supporting them as they grow to be important contributors to our company and to our clients.

With people at the centre of our growth and success, Work From Home has been a disadvantage. It didn’t stop us from delivering for our clients, but it did slow us down as an organisation. A big part of that slowing was the result of difficulties being faced by so many of our employees.

A return to growth

In January 2023 we started bringing the team back to the office. With a couple years of remote work experience throughout the organisation, we have been able to approach it with flexibility.

Some roles are able to split time between WFH and RTO, and most roles are able to WFH on the occasion where a stint of independent work aligns with current requirements.

Clarity and morale, which is really the keystone of continuity, were the first and most obvious wins as the bulk of our team returned to the office. No-one would say they enjoy a return to commuting, but the ease of working in a team that is physically present, in terms of efficiency, quality and camaraderie, does help ease the pain.

The WFH period has left us a more flexible team and a more resilient team. This increase in capability is contributing to a new burst of growth as projects and inhouse programs that were planned during COVID and were waiting on our return to the office are now being implemented.

Not every business needs to work from an office. And there are probably very few businesses that need every team member in the office every day of the week.

Looking at your business through the lenses of Clarity, Continuity, Capability and Growth, like we did, provides a clear structure for helping you decide where on the continuum between Work From Home and Return To Office your business needs to place itself.

How are LLMs like ChatGPT going to revolutionize real estate?

Here is how AIs like ChatGPT are going to revolutionise real estate: invisibly, by increasing efficiency in the market, and visibly, by making customer services a bit cheaper and higher quality.

AI is already in real estate, and has been for a while, but it’s not AI like ChatGPT. The AI in real estate is a collection of technology and techniques known generically as Machine Learning (ML) and it uses the same basic building blocks, called neural networks, that ChatGPT uses, but with different structures and different arrangements of the building blocks because they have a different purpose to ChatGPT.

Trulia is an example of a proptech company leveraging the power of ML. They’ve used it to build their property recommendation engine, for predicting user engagement, and for identifying property features from photographs.

These are things ChatGPT can’t do. ChatGPT is a Large Language Model (LLM). It’s a multi-layer neural network that has been fed a tremendous amount of text and it is very good at stringing words together into sentences in response to some input text.

It has been fed so much text that it is very good at masquerading as an under-trained lawyer, a sub-par business analyst, a mediocre marketer, a hack journalist, etc, simply by stringing together pieces of the text it has been trained on.

While being very good at generating text, LLMs like ChatGPT aren’t very good at math. You can’t use them to do things like predict user engagement or identify property features from photographs.

So what are LLMs like ChatGPT good for?

What LLMs are good at is bridging the vast gulf between human written text and the kind of numerical inputs ML systems can deal with.

This can be as simple as basic sentiment analysis, turning a review like “This is so not the best hamburger joint in town” into a -1, and a review like “I hate how good their fries are” into a +1 – simple numbers that can be used in a prediction model.

Stepping beyond this, LLMs can be used to turn text into ranks. An LLM can consistently turn, say, countertop descriptions like “mustard yellow formica”, “granite”, “hand-poured, hand-polished concrete” into a range from 1 to 10. The real value in this is that when it sees a new description, like “clear resin featuring embedded seashells”, it can fit it into the same range.

Moving beyond simple ranking, we can combine it with data extraction. Here is an example where an LLM pulls specific data from a police report. It could just as easily be a property listing:

This kind of “encoding” has normally been a job for humans. This drove up the price for the production and updating of useful databases, making them expensive, rare and proprietary. Spending the money and the time to do the encoding and compile a database was a nice way to build a moat.

But now it’s not a moat because anyone who can connect a web scraper (or web scraping service) to an API call to an LLM can fill a database with any information they want in days for next to nothing.

So getting data isn’t going to be much of a problem any more. That’s going to bring efficiency increases. Maybe some parts of the market will eke out a few more percentage points in returns, but only if they can use the data.

Trulia, for example, isn’t running their services on LLMs. LLMs will only be part of the pipeline. The real work and real value will still be done with classic ML models fed with higher quality data encoded by LLMs.

So, that’s the invisible part of AIs impact on real estate. Let’s now look at the likely visible impact.

AI, LLMs and email and chat

This visible impact is going to be based on the highly visible features of LLMs – their ability to generate text and respond in a conversational manner.

We’ve already seen too many real estate listing generators that use LLMs. That last link, agently, provides LLM generated property descriptions as one of their less important features.

This is one of the recurring gotchas of building products with LLMs. It is so easy to get text out of an LLM that whatever product idea you have, if it is just producing text then your business model is in danger of becoming another company’s bullet point.

Is AI Chat going to take over customer comms?

Chat is the big, consumer-facing application of LLMs like ChatGPT, Bing and Bard. With the ability to train LLMs on your business’s documentation via fine-tuning (OpenAI’s page on it) or using embeddings to constrain the information an LLM can work from, it seems we can all turn over our customer enquiries to software, saving us huge sums of money.

If only it were that simple.

The problem with using LLMs to “chat” to your customers is that you need to rely on raw text from the LLM. LLMs don’t have any judgement. They just generate text. The result is that they “hallucinate” – produce wrong or even wildly wrong responses – and there is no way to guard against this except having a human in the loop.

If you are building your customer service chatbot on top of an LLM like GPT-4, even with fine-tuning, the data you supply (chat logs, FAQs, blog articles) will be such a tiny percentage of the LLMs “knowledge” that it will inevitably “hallucinate” when answering questions. It might even insert information from a competitor’s products (that were documented on the internet prior to September 2021).

The other problem is that prompt jailbreaks – strategically written text that tricks the LLM into leaking information or hijacks it for the user’s own purposes – are not a solved problem.

The end result is that LLMs are not predictable enough to be trusted and so are not going to replace staff in customer service roles. Instead, they are going to act as an assistant to them. They’re going to help customer service staff find answers faster, they’re going to help customer service staff provide better answers, and they’re going to help customer service staff manage complex and ongoing interactions.

Crisp wrote up a fantastic deep dive into their efforts to add AI to their customer service. It’s not easy. It’s not cheap. It will get cheaper, but when you look at the steps they had to go through it is hard to imagine it getting easier.

Zendesk, the customer service SaaS, have also found that AI and LLMs are going to augment staff, helping to improve customer experience, instead of replacing them.

So AI chat as it is popularly imagined – replacing customer service staff with a bit of code – is not going to happen any time soon. For anything other than the most basic and pro forma customer enquiries, where an LLM chatbot might be limited to being a high-powered FAQ regurgitation engine, humans will still be in the loop and on the payroll.

What’s Proptech going to do with AI?

That’s the big question. The AI services landscape is already fracturing into service providers who will run LLMs for you, fine-tune them for you, host your data for you, etc. AI tools are generic tools. At the coal face they work just as well for any industry, be it real estate or livestock management. So the shovel sellers are already busy selling shovels.

Proptech startups need to be looking at what untapped data is out there, preferably in text format if you want to jump on the LLM bandwagon. That text could be in paper format. Have you noticed how good OCR has gotten in the last year or two?

The data might already be sitting on your server. A database of transactions is a prediction model waiting to be trained. Prediction gives you the opportunity to optimise and de-risk, two things every industry wants to pay for.

Whether you want to reduce the cost of comms with tenants or streamline nine figure real estate transactions or improve compliance in building maintenance schedules, you need to be asking yourself where you can scrape, collect, licence or buy the data you need.

That, we expect, will be easier to do than hire the data scientists you’re going to need to make it work. Maybe the shovel sellers will fix that for us.

How to harness AI and save your business from the future

Generative AIs like ChatGPT, Bard and Bing are changing the world faster than we can imagine. So fast that there are now ChatGPT-like AIs that can run on smartphones. So fast that the cost of training a ChatGPT-like AI has dropped from $4.6 million in 2020 to $450k today. And it’s happening so fast that startups are seeing their business model trashed by Google and Microsoft before they can get traction. The speed of change is making people suspect OpenAI is using ChatGPT to speed up their development of new AI and features.

As a startup or a small to medium business generative AI is going to accelerate and empower you. You and your team are going to work smarter and work faster. You’re going to do more with less or grow and do much, much more than you imagine possible.

AI is going to make it possible for a lone founder to do what a medium-sized company does today, and it will allow a medium-sized company to do what right now only a big company can do.

This shift in ability to execute is making people worried that jobs are going to be lost as everyone incorporates AI into their workflows. However, as has been pointed out by many commentators, if your revenue per employee keeps going up as they complete work faster and do more using AI, why would you fire anyone?

How much of a change in productivity will we see? In a draft of a working paper titled “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models” released on March 27, 2023 by Eloundou et al, the following estimations are made:

“Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. … The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. … Our analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality. When incorporating software and tooling built on top of LLMs, this share increases to between 47 and 56% of all tasks.“

To help you understand how you can make the most of the changes generative AI is going to bring we’re going to start with the basics and give you a quick background on ChatGPT and how it and other generative AIs work (this includes Microsoft’s Bing and Google’s Bard).

After that we’ll go through the ways you can take advantage of AI and not be left behind.

A simple introduction to ChatGPT

To paraphrase a random internet commenter – “People would be shocked if they understood how simple the software behind ChatGPT really is”. If you’re technically minded this will show you how to build something similar to ChatGPT in 60 lines of code. It can even load the data used by GPT-2, one of ChatGPT’s predecessors.

ChatGPT was built by OpenAI. It’s a type of Large Language Model (LLM) and part of the class of AIs called “generative AI”. A language model is a computer program designed to “understand” and generate human language (thus “generative AI”). Language models take as input a bunch of text and build statistics based on that text – things like which letter is most likely to appear next to the letter “k” or which word is most likely to come before “banana” – then use those statistics to generate new text on demand.

When a language model is generating text, like in response to a question, at the most basic level it is simply looking at the text, in this case a question, and using the statistics it has generated to choose the word most likely to come next.

A Large Language Model (LLM) is just a language model trained on a large amount of text. It is estimated that the LLM that underlies the initial version ChatGPT, GPT-3, was trained on 300-400 billion words of text taken from the internet and books. That training was, basically, showing it a word from a document, like this article, and showing it the approximately 100-500 words that preceded it in the document (only OpenAI knows the actual number).

So if an LLM was fed this very article, it might be shown the word “human” and also the words “ChatGPT was built by OpenAI … and generate” that led up to it.

It turns out that when an LLM is fed nearly half a trillion words and their preceding text to build statistics with, those statistics capture quite complex and subtle features of language. That isn’t really a surprise. Human language isn’t random. It has a predictable structure, otherwise we couldn’t talk to each other.

It’s not just predictable. There is a lot of repetition. Repetition in the phrases we use, like “how’s the weather”, but also in sentence structure, “The cat sat on the mat. The bat spat on the hat.”. Even document conventions. Imagine how many website privacy policies ChatGPT would have been trained on by using the internet as a source of text.

How ChatGPT generates answers

When you ask ChatGPT a question, the words of your question become the preceding text for the next word. This preceding text is called the “context”. It’s also known as “the prompt”.

Your question, the context, is used by ChatGPT to find the most statistically probable word that would begin the answer.

Let’s say your question is “Why is the sky blue?”. First, imagine how many times that question appears on the internet and in books. ChatGPT has definitely incorporated it many times into its statistics.

“Why is the sky blue?” is a 5 word question, and forms the 5 word context. So what is the 6th word of the context going to be? It’s going to be the word most likely to appear 5 words after “why” in all the text ChatGPT has ever seen, as well as 4 words after “is” and 3 words after “the” and 2 words after “sky” and 1 word after “blue”.

(The question mark is also important, but we’re ignoring that for this simple explanation)

That word, the most probable word to fit all those conditions at the same time might be “The”. It’s a common way to start an answer. Now our context has 6 words:

“Why is the sky blue? The”

And the process is repeated:

“Why is the sky blue? The sky”

and repeated:

“Why is the sky blue? The sky is”

“Why is the sky blue? The sky is blue”

“Why is the sky blue? The sky is blue because”

The context grows one word at a time until the answer is completed. ChatGPT has learned what a complete answer looks like from all the text it has been fed (plus some extra training provided by OpenAI).

You may have heard about “prompts” and “prompt engineering”. Because every word in the context has an effect on finding the next most probable word for the answer, every word you include will act to constrain or shape the possibilities for the next word. For example, here is a prompt and answer from ChatGPT:

Add a few related words and the answer shifts in a predictable way:

This, in a nutshell, is what prompt engineering is about. You are trying to choose the best words to use to constrain ChatGPT’s output to the type of content you are interested in. Take a common prompt like this:

“Imagine you are an expert copywriter and digital marketer with extensive experience in crafting engaging and persuasive ad copy for Facebook ads. Your goal is to create captivating ad copy for promoting a specific product or service”

Don’t be fooled into thinking that there is some kind of software brain on a server in a giant data centre in the Pacific Northwest imagining it is an expert copywriter. Instead, think of all the websites run by copywriters and digital marketers and their blog articles where they discuss Facebook ads or writing engaging copy.

You can read OpenAI’s guide to prompt engineering here, and this guide to prompt engineering goes even deeper.

It is without doubt amazing that ChatGPT does what it does. It is also amazing that the process is so deceptively simple – looking at which words come before other words. But it takes nearly half a trillion words of human-to-human communication to provide the data to make it happen.

This is a simplification and leaves out important details, but what you need to know is that generative AIs like ChatGPT, Bard and Bing are always only choosing the next most likely word to add to a reply. It’s a mechanical process prone to producing false information. Added to this unavoidable feature of blind, probabilistic output, to make generative AI output more “creative” or “interesting” actual randomness is added to their choices.

Generative AIs have been trained on enough logic and reasoning examples to mimic how we use language to communicate logic and reason. But they are, in the end, text production programs. There is no logic or reasoning as humans use it involved in producing that text. Even if the text contains a logical argument. So always review carefully what text they produce for you.

As one person who spends a lot of time producing text said:

Or is ChatGPT actually smart?

Having said all that, there is an argument that generative AIs, in particular LLMs, might be doing more than just producing text. This argument says they might be building a model of the world and the features of the world as they build their billions of statistics about text. And that these models might be what is making generative AIs so powerful.

Giving some partial support for this argument is the number of “emergent” abilities generative AI is showing. The backers of this argument say these are proof there is more going on than picking which word should come next. The emergent abilities are quite specialised. Such as naming geometric shapes. It’s not like it is teaching itself how to pilot a plane. You can find a list of the emergent abilities documented so far in this article. Be warned, they aren’t very impressive.

How to use generative AI and ChatGPT in your business

For these next sections we’re going to mostly refer to ChatGPT, but it applies to any publicly available generative AI including Google Bard and Microsoft Bing.

Use ChatGPT to work fast and smarter

ChatGPT has ingested more pro forma correspondence, business documentation, business books, corporate communications, RFPs, agreements, contracts, pitch decks, etc, than you can possibly imagine.

This makes it the ultimate tool for producing the first draft of just about any document, including replies to emails, white papers, case studies, grant proposals, RFPs, etc. It can also serve as an editor, helping to turn your rambling sketch of an email or an article introduction or anything into clear, coherent sentences and paragraphs you can further revise.

With ChatGPT you never have to delay writing an email or starting a document because you don’t know where to begin. Or because you’re completely out of your comfort zone, in over your head, or have no idea what you’re supposed to say. ChatGPT has seen it all and can help you write whatever you need to write.

Now, this does come at a slight penalty, which for most things will not matter now, and in the long run will probably be welcome: ChatGPT has a distinctive “voice” that if you’re familiar with it will be noticeable.

Also, because ChatGPT is always choosing the most probable word each and every time, what it outputs can be quite boring or cliché. Sometimes this is a good thing. Clear communication is based on convention. But don’t expect creativity or original ideas.

ChatGPT is perfect for streamlining your production of all those necessary business communications that humans use to keep things running. By adopting ChatGPT as part of your process, you will be able to execute on these faster and at a higher level, leaving you more time for the work that really moves the needle.

If you’re not sure how to command ChatGPT to produce what you need, this prompt engineering guide will help.

Of course everyone else you interact with will be doing the same thing. So expect the speed of business to increase and hope all your third parties prompt ChatGPT to keep their emails brief.

As Sam Altman, the CEO of OpenAI tweeted:

Use 3rd party AI tools to accelerate your business

In this section we are going to focus on data and text related tools. The AI-generated image and video space is also huge: Dall-e, Midjourney, Adobe Firefly, Stable Diffusion, etc. There are hundreds of them, but for most businesses image and video is a part of marketing rather than their core product, so we’re sticking with the most common use cases.

There are already lots of startups offering AI-powered tools of every variety. But they’re about to face the twin behemoths of Google WorkSpace and Microsoft Teams. Both have recently announced the integration of AI assistants into their offerings (Google’s, Microsoft’s).

How will these work? Imagine ChatGPT knows everything about your business. It has memorised every report, every spreadsheet, every presentation, and every email. You can ask it for numbers or summaries or ask it to create presentations or documents.

Some of these features will help reduce the time you spend on the boring necessities of keeping your business running. Others, like integration with spreadsheets, will help you find answers, create forecasts and analyse trends faster and easier than you could before. It’s even possible you can’t even do regular forecasts because your team has no-one with the expertise. That’s going to change.

Again, this is going to give you more time to spend on doing the things that really make a difference to your business – planning, strategy, talking to customers, building relationships with partners. Unless you make the mistake of burying yourself under all the reports it will be so easy to create. But you can always ask the system to summarise them for you.

Non-Google, Non-Microsoft AI tools for your business

At the time of writing there isn’t even a beta program for Microsoft and Google’s AI-powered offerings. They haven’t provided a rough date when they will be generally available. On the other hand, lots of startups are developing services based on OpenAI’s APIs, using the same LLM behind ChatGPT to create new products.

The site Super Tools has a database ]AI-based startups. You might be able to find some products in there that can help you.

If we continue to focus on text (Super Tools includes image, audio and video tools as well) these products fall into two main categories of functionality: content generation and search.

Content generation covers things like chatbots, writing assistants and coding assistants. Some of these services are nothing more than a website that adds a detailed prompt (or context) to be sent along with your own instructions/queries to ChatGPT’s backend and the response is then passed back to you.

An example of this is VenturusAI. At least it’s free. Think of these services as a lightly tailored version of the standard ChatGPT experience already provided by OpenAI. This might be obscured by design or presentation. A few hours fiddling with a prompt in OpenAI’s ChatGPT interface might get you the same result without the cost of another SAAS subscription.

If the output is short enough, and like VenturusAI they’re nice enough to show you example results, you can just paste their examples into ChatGPT and ask it to duplicate the result but for your own inputs.

Content generation with AI

Content generation is already impacting programming, legal services, not to mention copywriting of all kinds, including real estate and catalogue listings.

The impact of content generation tools is already being felt. According to Microsoft, for projects using their Github Copilot code generation assistant, 40% of the code in those projects is now AI generated. Given that code probably took a fifth or a tenth of the time it would take a normal programmer to write it, the productivity increase is enormous.

Smarter search with AI

Search is just what it sounds like, but imagine a search engine that’s smarter than Google and can respond to your search request with exactly the information you need written in a way that’s easy to understand.

Dedicated search tools are springing up based on OpenAI’s APIs. Some target specific use cases, like Elicit for searching scientific papers, others, like Libraria, are more general – upload any documents you want and it’ll index them and give you a “virtual assistant” to use as a chat interface to query them.

Build your own AI tools to power your business

There is no reason you can’t use OpenAI’s APIs yourself. They offer methods to fine-tune a model and to create embeddings. You’ll need a programmer to do this. Or, if you’re feeling brave and/or patient, you can ask ChatGPT to help you build a solution.

Fine-tuning Large Language Models

Fine-tuning uses hundreds of prompt-response pairs (which you supply) in order to train an LLM to do things like answer chat queries based on information you care about. For example, you may get the question-response pairs from transcriptions of customer service enquiries. You upload them to OpenAI. It uses them to create a new, specialised version of one of their base models that is stored and runs on their servers. Once it is built you use the API to pass chat-based enquiries to your dedicated model and get responses back in return.

If you’re clever, some of these responses contain a message signalling that a human is needed to deal with the enquiry and your chat system can make that happen.

Embeddings for semantic search

Embeddings are used in querying across large amounts of text. Imagine asking normal language questions about information hidden in your company’s folder of reports and getting back answers.

For example, if you have hundreds of PDFs with details about the different models of widget you produce, you can create embeddings for all the documents, and then you will be able to ask things like “Which widget is best for sub-zero environments?” or “Which widget is green and 2 metres long?”. Even better, your customers will be able to ask those questions themselves.

These next few paragraphs give a basic information on embeddings and how they are used. You might want to skip it the first time through.

Embeddings are basically a list of numbers. An embedding can be thought of as an address in “idea space”. Instead of having 4 dimensions like the space we live in, this “idea space” has over 1500. Every word or chunk of text can have a unique embedding generated for it by the LLM. Texts that are conceptually close together will have embeddings that are close together (based on a distance algorithm that works for 1500 dimensions).

For example, the embeddings for “apple” and “orange” will be close together because they are both fruit. But the embeddings for “mandarin” and “orange” will be even closer together because they are both citrus fruits.

Surprisingly, this also works when you get the embeddings for hundreds of words of text.

Once you have embeddings for every chunk in every document you want to search stored in a database that can do those multi-dimensional distance calculations, like pinecone, or FAISS, you’re ready to do the actual search.

This is a bit clever. To do the search, you take the user’s query, and with a selection of pieces of your documents you send it to your LLM, like ChatGPT, to generate an answer that is probably not completely correct but is close.

Then you get the embedding for that answer and use that to search your database for the chunks whose embeddings are closest in distance to it. You can then either present the pieces of document to your user, or send the question and those chunks (limited to the size of the allowed context), to ChatGPT for it to generate a properly structured answer.

This article goes into more details and strategies on using embeddings.

If you’re more technically minded, you can connect ChatGPT to any number of tools using a library called Langchain. It cleverly uses prompts to direct ChatGPT to output calls to external services, like calculators, databases, web searches, etc, and Langchain handles call the service, collecting the results and then adding them to the current conversation with ChatGPT.

This pre-dates and is similar to OpenAI’s plugins, but it’s more versatile and can be tailored to your specific needs.

Try and use agents to do your work for you

Using LLMs to create autonomous agents has grabbed a lot of recent attention. By integrating a generative AI like ChatGPT into a system that can do things like search the web, run commands in a terminal, post to social media and other actions that can be driven by software, you can build a system that can make simple plans and execute them. Kind of.

These systems, like AutoGPT and BabyAGI work by using a special prompt (that you can see an example of here) that tells ChatGPT what it is to do and includes a list of external commands it can call. It also tells it to only output information in JSON format instead of human readable text – so it can be easily read by other programs.

AutoGPT feeds the prompt to ChatGPT and collects its response in JSON format. It executes any commands it finds in the response and incorporates the results from those to create the next prompt to feed to ChatGPT.

It uses ChatGPT’s context, which is about 3000 words, to create a short term memory for ChatGPT that can hold the goal you’ve given it, the remaining steps in its “plan” and any intermediate results it needs. This makes it somewhat capable of devising and executing short plans. We use the word “somewhat” because it needs constant monitoring and errors can cause it to go off track.

The short term memory can be extended using embeddings as we discussed above. And there is an active community working hard to make the LLM agents more robust and more capable. But for now, you may be able to use an agent to automate a simple workflow. It is particularly good at compiling and summarising information from the internet. Just be sure to do lots of testing and don’t make it an essential part of your infrastructure.

Use no-code/low code to build inhouse AI tools

Because the OpenAI API returns JSON for your requests, you can access it from all the best no-code/low code app builders which support third party APIs.

If you don’t need apps but just want to integrate generative AI into your workflows, Zapier and Make now support OpenAI in their integrations. You can use it to automatically draft emails or generate customer service tickets based on incoming emails. Anywhere in your workflows where a human has been needed to make a basic planning or routing decision is a candidate for being automated now.

Build your own generative AI startup

Using the same tools and techniques you would use to build inhouse AI tools you can build a product.

But that’s just the first level. Beyond leveraging OpenAI’s APIs you can use services like Cerebras to build and train your own model.

Training your own model might be out of reach, but fine-tuning an existing model might be all you need. Fine-tuning a model for a specific domain already has a name – “models as a service”. These models can help users in a particular domain do everything from fix their spelling to estimate the cost of repairs to design new molecules. Of course your use case will dictate the model you fine-tune which will impact your costs.

The power of having your own fine-tuned model that users interact with is that once you have users you now have a source for even more training data, creating an ongoing cycle of fine-tuning and model performance improvement that can build a moat for you in your market.

Go forth and generate

We hope this article has given you the understanding, inspiration and links that you need to get started using generative AI in your business.

Start small, use ChatGPT to draft a few emails (but double check them). Browse Super Tools and see if there are any tools on there that might address one of your workflow or business process pain points.

Small steps and strategic integration of generative AI is key to taking advantage of this huge technological leap forward. Start today and see how fast and far it can take you.

The best no code / low code tools and strategies for building your MVP

This is a quick rundown to help you choose the best no code/low code tool to start building your MVP and the best strategy to follow.

To keep it simple we’re just going to call them no code tools. No code tools can be divided into 3 main categories – workflow automation for business/enterprise, website builders, and app builders. We’re going to focus on app builders.

The best no code tools for building apps

After running through hundreds of pages of documentation and community forum posts as well as days worth of tutorials, these are the app builders we would recommend:

None are perfect. They all have their own unique pain points. But they all follow a similar model for structuring an app. If the functionality that your MVP requires can fit within that structure you will have little trouble producing a working prototype in weeks instead of months.

The glaring limitation of no code tools

Here is the main reason why no code tools are best used for building prototypes and MVPs and why you will eventually turn to or integrate custom code as your app matures:

No code tools focus on the lowest common functionality, abbreviated as CRUD.

CRUD stands for “Create, Read, Update, Delete”, which are the four operations you can perform on a database, which is where your app is going to store any data.

How this looks in an app like a marketplace:

Users CREATE a new item to sell in a marketplace
Users browse – READ – items available to bid on in a marketplace.
The current highest bid on an item is UPDATEd when it is made.
If the seller abandons the auction the item is DELETED.

UX limitations of no code tools

No code tools are further limited in that the interfaces you can build with them tend to be restricted to displaying: 1) static pages, 2) a list of items pulled from a database (like a list of homes for sale in the suburb of Erinsborough), and 3) a detailed view of a single item pulled from a database (like that home on 24 Ramsay Street).

They’re basic standard views. Exactly what you would use in a prototype to make sure you’re pulling the data you expect. While they can be designed and tailored for your app and brand, your UX is still being decided for you.

If you find this too limiting you will need to look towards more advanced no code tools that allow you to also do low coding (or even full coding) like FlutterFlow or Draftbit. But should you code portions of your app from scratch? Do you have the inhouse skills? Can you hire someone who is familiar with your tool of choice?

Obviously, for an MVP, the decision to expand from no code to low code will delay launch and should be considered carefully. Once you start coding your options become unlimited, but so does the time it will take to finish your app unless you have access to the talent to write the code and the discipline to manage your feature list.

The two no code strategies – Build to throw away / Build to keep

If you want to validate your product market fit as quickly as possible, then you can build your MVP using a no code tool with the intention to throw it away and rebuild later from scratch when you have found product market fit.

This gives you the speed of a no code tool. That speed makes it easier to avoid the sunk cost fallacy so you feel okay throwing it away and starting again from scratch using proper tools and a proper framework.

“Throwing it away” isn’t literal. You’ll keep it running while you build the next version.

If you already have users and a market and want to provide an app, then you want to use a tool that allows you to continue to grow your app’s functionality beyond what the drag and drop interface provides. You want to build to keep.

If you want to build to keep make sure you choose a tool that gives you the features you need to implement the complete product you envision. No code tools are simple because they are often limited.

To avoid these limitations you will want to use a no code tool that lets you export the code for your app. See the end of the article to see which tools allow this.

Which no code tool gives you the best chance for success?

Success depends on finishing your app and getting it into the marketplace. Your best chance for success depends on matching the no code tool to your technical expertise. Here is what we would recommend for different levels of programming ability. Choose the one that best describes you:

Excel power user – Adalo
Designer who leans tech – Bravo Studio
Has some understanding of coding -Thunkable
Writes the occasional script – Draftbit or Flutterflow
Software developer – Draftbit, Flutterflow or Backendless

Of course there’s nothing stopping you from using a less complicated no code tool. It might give you a speed advantage.

Comparing the no code tools mentioned in this article

Like all options in a crowded marketplace, every choice has its trade offs. This table summarises what we think are the main factors that should guide your decision on choosing a no code tool.

No code may be all the code you need

No code tools continue to add features (like integrating third party APIs) as they compete with each other. What can be built with no code tools today couldn’t be built a year ago.

But don’t let possibility fool you into making your MVP more complex than it needs to be to launch. If you can work within the standard set of CRUD actions combined with list and detail views (which 9X% of apps can) then you will be able to get your MVP to launch with any one of the tools we discuss.

As your experience with no code tools and your understanding of your app grows, you might find it worthwhile to move up to the more powerful no code tools on the next iteration. Combined with a no code backend, you may be able to delay the transition to bespoke code until revenues are high enough to make cutting platform costs worth the investment.

Whatever path you take to build your MVP with no code tools, it’s sure to be shorter than the alternatives.

Freelancers vs team extension – why freelancers always lose

IF you’re an SMB that needs to step up technically, either by building an app, creating a website or even just updating your online presence, this article might save you from freelancer frustration.

As an SMB you’re always at a disadvantage when it comes to tech. You can’t afford to have the expertise you need in house, and hiring freelancers is an exercise in frustration. How are you to stand out in your market, or even compete in it, when tech is now a major factor in success?

We’re going to fill you in on the advantages of a team extension versus hiring freelancers. And you’re going to be glad you’ve learned about it.

What is a team extension?

A team extension is basically remote workers as a service. A team extension can be a single software developer working a few hours a week to a full stack dev team numbering in the dozens.

What differentiates a team extension from outsourcing is that the members of your team extension report directly to you and they are integrated with your in house team, tools and systems. They attend meetings (virtually), they work directly on your projects, and, if they are full time, they work only on your project.

One of the biggest benefits of the team extension is that you have the full support of your team extension service provider. They want you to succeed with their team members as much as you do.

How is hiring for a team extension different from hiring freelancers?

Hiring freelancers requires you to become an expert in IT recruitment. Choosing the wrong freelance software developer can sabotage your plans.

When hiring for a team extension you are hiring out of a proven talent pool with a track record of helping businesses like yours build apps and websites and keep their tech infrastructure running.

With hundreds of completed projects under their belts, your service provider knows the skill set you need and can help you estimate the hours or headcount as well. They are also able to put forward proven candidates for you to interview and pick from.

And unlike with a freelancer, if someone doesn’t work out you don’t lose time or code. You can work with your service provider to rapidly replace them and keep moving forward.

A team extension builds your product, a freelancer sells you code

This is a subtle but important difference. A team extension really is an extension of your team. They’re like employees working out of a different office. They’re output is your output. At all times you have complete visibility into their progress and the work they’ve completed.

Freelance software developers, for good reasons, tend to deny access to any code they’ve written for you until they have been paid in full. There might be online demos they’ve built for you to comment on, but the code remains untouchable.

This gives your project a “bus factor” of 1. Which is how many people need to be hit by a bus to derail your project. Or how many people need to get sick or get sick of the project, to derail it.

With a team extension you have an entire organisation supporting you and your team members. All the code being written is yours and is always accessible. You don’t need to worry about “bus factor”.

Your team extension, and you, get cost and management support

Another major benefit of a team extension is that you get the increase in headcount and productivity without the increase in office space, resources and HR burden.

Your team extension members work out of the service provider’s offices. They supply the workspace and the equipment. They also provide all of the HR and employee support services required. This includes all kinds of work management, training and personal support.

The situation for you is not unlike becoming a team leader where upper management is devoted to helping you get the results you need. You have a second level of oversight to help you successfully negotiate all the challenges of the increased headcount.

What could you build with your dream team?

If you’ve been unable to move forward on the app or website that your business needs because of the challenges in hiring developers, we hope this article has opened some doors for you.

You can build the app or website you need. You can do it while minimising the risks by adopting the team extension model.

If you want to discuss extending your team with some of our talented developers don’t hesitate to get in contact with us.

Building your app with an extended team

Getting Started

Briefing with SoftwareSeni about the product you want to build

We work with you to sketch out the development strategy for your app, web app or website.
Decide on a tech stack based on your functional requirement
Start reviewing developers from our talent pool

You start with a shortlist of talent that you can be sure have the skills and experience you need. You meet one-on-one for a video interview so you can get to know the candidates.
Complete scoping and specification

Wireframes and business rules are finalised. Feedback during the process ensures balance between budget, features and viability. Full project estimates are in place. You know exactly what needs to be built.
Engage and onboard your chosen developers

Development begins

Weekly sessions with your SoftwareSeni product consultant and project manager keep the project advancing smoothly.

Review product prototypes and give feedback
Stay in the loop

You get daily updates as well as weekly and monthly reconciliation reports so you always know how the project is tracking financially and towards your deadline.
Developers integrate your product with the services your business relies on

This includes payments, CRM, ERP, customer support, reporting, shipping, and more.
Infrastructure is configured ahead of launch

Web servers, database servers, load balancers, CDNs – all the technical services you need are set up, tested, and placed under monitoring.
Final rounds of QA are completed on staging servers
Soft launch on production servers with a small cohort of beta users

Another round of full QA is conducted to ensure there are no incompatibility issues in the live environment.

Launch!

The first version of your product goes live. Your marketing campaign goes into action. You start signing up users.

Post-launch

The first version of your product goes live. Your marketing campaign goes into action. You start signing up users.

Our Support team keeps your product online

Need 24/7 uptime? Our team can monitor and respond to any incidents as they occur. Traffic to your product grows and it begins to generate revenue for your business.

What’s next?

Your extended development team begins to work on the next round of features

The hard work is done so you scale down your team.

Your smaller team continues to add features at a pace that matches your budget and incoming revenue.

Call a product freeze

Release your extended development team, but retain a part-time developer familiar with your product to handle dependency updates, API changes, service provider issues, etc.

Expand your customer services team

Just like your extended development team, use a team extension for customer service to handle the increased enquiries and interest following your successful product launch

How to recruit software developers in days instead of months

Despite all the tech layoffs it’s never been harder to find, filter, and hire experienced developers. Demand is through the roof for these roles, making recruitment long and arduous while all you want to do is get your app to market and keep revenue coming in. There is a shortcut that will get you the dev talent you need, but you will have to think outside the box you’re in.

There really is a developer shortage

And it’s getting worse. In the US, software engineering positions are expected to increase 22% by 2030. That means about 190,000 new jobs opening per year. But there are only ~53,000 computer science graduates per year. Even with the tech layoffs in January 23 of roughly 40,000 that still leaves a shortfall this year in the US of 100,000 jobs.

The same thing is happening in Australia. There will be 100,000 unfilled software related positions by 2024. But Australia is graduating only about 8,500 (domestic) computer science students per year.

No wonder it can take months to recruit a software developer.

Use a team extension to hire developers fast and affordably

Team extension, also called a dedicated team, extended team or extended development team, is basically remote workers taken to the next level – featuring dedicated management, lower risk, and reduced costs.

In a team extension you work with a team extension vendor like SoftwareSeni to access their pool of software engineering talent.

In SoftwareSeni’s case, we use a mixed onshore/near-shore model. You work directly with a project consultant based in Sydney, Australia. The SoftwareSeni talent pool is based in Yogyakarta, Indonesia’s tech hub. This provides a large time zone overlap for Australian businesses that makes developer integration relatively painless.

To increase your team headcount you discuss with us your project and the skills and experience you’re looking for. SoftwareSeni then provides a shortlist of candidates for you to interview and select from. What can take 2 or 3 months (including negotiating salaries and paying $$$ to recruiters) is completed in as little as seven to fourteen days, with you knowing at the outset what your costs will be.

How is a team extension different from outsourcing?

In this era of remote work a team extension is no different to normal team members. The developers show up. You manage them and assign them work. They do the work. They attend meetings virtually. Just like the rest of your team.

However, unlike other remote team members, they work out of the vendor’s premises. SoftwareSeni provides a second level of supervision that your other remote team members won’t have. This helps to ensure delivery of the high quality of service we provide all our partners. We also provide your team extension developers with technical and human resource support functions you don’t need to fund or deal with.

Unlike a typical outsourcing arrangement, you are in complete control and have complete overview into the work your team extension developers are doing for you. And you can always talk to your SoftwareSeni project consultant if you’re not sure or have any questions or need insights.

How to start the rapid hiring process for your next developer

Start the process to hire your next developer in days by speaking to us. At SoftwareSeni we can help you to quickly finalise experience and skill set requirements based on your product goals so you can keep moving forward. At every step of the hiring process you’ll be in complete control of decisions and costs and be able to scale your team extension options up and down to match your budget.

How to get your app to market faster

The best time to get your app to market is yesterday. Great advice. Not very actionable. More actionable: get your app into the market as fast as you can. If you know you’re already working at your limits, we’ll show you how to get your app to market faster with one phone call. It’s not a secret – a big part of it is an extended development team.

Prioritise the right features of your app to launch sooner

The first step to getting your app to market faster is focusing on the Minimal Viable Product (MVP). The MVP has the smallest set of features that will deliver value to your customers and start generating revenue for your business.

This means at launch maybe they won’t be able to set a profile picture in the MVP, but they can login, view items and order. That’s what they really want to do with your app. And that’s what you need them to be able to do.

After launch you continue to iterate on your MVP, adding new features and improving existing functionality based on user feedback. Done right, this leads to increasing revenue and uptake of your app, making its ongoing development self-supporting.

Use an extended development team to get your app to market faster

There is a piece of folk wisdom in software engineering that says throwing more programmers at a problem will only make things take longer. This originates in Fred Brooks’ book The Mythical Man Month. Don’t worry about it. It’s true(ish) under certain circumstances. But developing a new app is not one of them.

As an app can be divided into a front end (the user interface) and a back end (that manages databases, authentication, integration with other services, etc), as well as processes like QA, devOps and provisioning infrastructure, there are multiple focus points where specific expertise, or simply more devs, can move your app forward.

It takes deep pockets and months of interviews to build a team that can hit all these areas at the same time. This is out of reach for most SMBs unless they make use of an extended development team.

The lack of in-house talent results in a long, slow path to launch, often delayed due to missing expertise. Most programmers can learn most programming related skills – languages, frameworks, algorithms, tools – but it takes time and it takes even longer to become an expert.

So, yes, your React programmer could optimise your database queries, but it would be faster and less error prone to hire a developer who already knows exactly what to do.

This is where the extended team comes in. Also known as a team extension, dedicated team or staff augmentation. An extended development team is a lot like remote workers but with better management, lower risk, and reduced costs.

One of the biggest benefits of an extended team is how quickly they can be put in place and start being productive. If you’re trying to get your app to market fast, this can save you months. This quick set up is made possible by the vendor you will work with.

How do you hire an extended development team?

Hiring an extended team requires a vendor like SoftwareSeni. In SoftwareSeni’s case, we use a hybrid onshore/near-shore model: your consultant is based in Australia, and our talent pool is located in Indonesia’s tech hub, Yogyakarta.

To hire your team you supply us with details on the skills and headcount that you need and SoftwareSeni provides candidates for you to interview. What can take two or three months (including negotiating salaries and paying fees to recruiters) is completed within a couple of weeks. And before you begin interviewing you already know what your costs will be.

How is an extended development team different from a remote team or outsourcing?

In this post-Covid era of Work From Home, an extended team is no different to a remote team. They show up (online). You manage them. They do the assigned work. They attend virtual stand-ups and meetings. Just like the rest of your team.

However, unlike normal remote employees, they work out of the vendor’s premises. This provides a secondary level of management at no extra cost to you. The vendor wants the developers in your extended team to deliver exactly what you need. They also provide your extended team members with technical and human resource support functions you don’t need to fund or deal with.

Unlike a typical outsourcing arrangement, you are in complete control and have complete insight into the work your extended development team members are doing for you.

Move faster and do more with an extended team

An extended team lets you increase your headcount, and development pace, without increasing your budget. The extended team also provides you with a level of flexibility a normal team cannot.

You can drop team members when their skills are no longer needed. You can add staff to areas that need more attention. All while having complete control over your budget.

Reduce your time-to-market with a phone call

The first step in getting your app into the market faster is to speak to your vendor. At SoftwareSeni we can help you to quickly finalise team composition and skill sets based on your product goals so you can start building faster. At every step you’ll be in complete control of costs and be able to scale your team up and down to match your budget.

Contact SoftwareSeni to start building your extended team and getting your app to market sooner.

How to build the dev team you need with the budget you have

Get ready to master the one simple trick™ that will help you build the roster of tech talent your business needs – the extended development team.

There aren’t enough developers to go around. The situation is going to get worse as software engineering positions are expected to increase 22% by 2030. Here’s what the math looks like around that:

Current number of dev jobs in the US: 1,847,900

New jobs per year: ~189,000

Computer science graduates per year: ~53,000

Shortfall: 136,000 per year

Those are US numbers. In Australia, the situation isn’t much better. Research from 2021 places the number of developers and related roles at 630,000 with estimates that there will be 100,000 unfilled positions by 2024. At the same time, Australia is producing only about 8,500 (domestic) computer science graduates per year.

The shortfall in available talent is driving up salaries. Salary levels are being further distorted by the willingness of large companies like Meta, Apple, Microsoft, Alphabet, and Amazon (MAMAA) to pay high salaries along with stock options to fill their open roles and retain talent. The post-COVID embrace of remote work means these salaries are affecting markets even outside of the US.

If your business needs an app to compete, or your business is an app, this is the market you are fighting in to land the talent you need to survive. The competition for developers means it can take 2 to 3 months to fill a role with the right person. If you can afford them.

What is an extended development team?

An extended development team, the “one simple trick”, also goes by the name of a team extension, dedicated team or staff augmentation. It’s remote workers with better management, lower risk, and lower costs.

The biggest benefit of an extended team is how quickly they can be put in place and start being productive. This is made possible by the vendor you will work with.

How do you build an extended development team?

Building your extended team will require a vendor like SoftwareSeni. In SoftwareSeni’s case, we use a hybrid onshore/near-shore model: your project consultant is based in Australia, and our talent pool is based in Indonesia’s tech hub, Yogyakarta.

To build your team you simply supply us with details on the composition of the team that you need and SoftwareSeni provides candidates for you to interview and select from. What can take 2 or 3 months (including negotiating salaries and paying $$$ to recruiters) is completed within a week or two, with you knowing at the outset what your costs will be.

How is an extended development team different from a normal team or outsourcing?

In this era of Work From Home, an extended team is no different to a normal team. They show up. You manage them. They do the work. They attend meetings. Just like the rest of your employees.

However, unlike normal remote employees, they work out of the vendor’s premises. This provides a second level of supervision. The vendor wants them to deliver the quality of service they agreed upon with you. They also provide your extended team members with technical and human resource support functions you don’t need to fund or deal with.

Unlike a typical outsourcing arrangement, you are in complete control and have complete insight into the work they’re doing for you.

Where to start when building an extended team?

The first step in building your extended team is to speak to your vendor. At SoftwareSeni we can help you to quickly finalise team composition and skill sets based on your product goals so you can start building. At every step you’ll be in complete control of costs and be able to scale your team up and down to match your budget.