My Teach Yourself Dabbling Shelf

The Great Teach Yourself Refresh

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I don’t know if you clocked it as well, but my favourite blasts-from-the-past Teach Yourself have gone and given their flagship language books a bit of a refresh.

Teach Yourself is a language learning brand with a huge heritage. If you want to track how language tuition has changed (and stayed the same!), then vintage editions are a great place to start. For nigh on a century they’ve been the cornerstone of the self-teach market, appearing in 1938 and quickly establishing itself as the brand for curious minds – not least those interested in foreign languages.

Teach Yourself advert in The Bookseller, 1959

Teach Yourself advert in The Bookseller, 1959

From quite traditional grammar-based handbooks, there was a noticeable turn towards a more communicative approach in the 80s, which kept them relevant right into the current era. That’s not to say there’s not life in those old versions – I personally love the chalk ‘n’ talk grammar translation method (although it has a time and a place!).

My Teach Yourself Dabbling Shelf

Some of my vintage Teach Yourselves!

In any case, they’ve seen fit to give their mainstream offering a revamp this year – and of course that means I’m going to have to get them to keep my collection complete. French, German, Italian and Spanish appear in smart, significant rewrites, all for release later this month. Not only that, but the Teach Yourself Beginners series for entry-level language has had a makeover, with titles ranging from Greek to Korean and Portuguese – and most of them are already available for purchase.

It’s a move that keeps the brand up-to-date, and still competitively priced, too – they compare very favourably with other popular course names. Well worth a look if you’re in the market for a new language – or just want to add them to your collection, like I do!

Image showing lots of document icons for a post on building a Zotero and Obsidian workflow

Zotero and Obsidian : A Workflow to Research Anything

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If much of your study is electronic – e-books, PDF papers, worksheets and the like – you’ll face the same struggle I have: digital overwhelm. A clear workflow for dealing with mounds of virtual material is essential if you’re not to get lost.

I feel like I’ve tried them all, too. I’ve gone through the gamut of e-readers: GoodReader, PDF Expert, even trusty old Apple Preview (which has great annotation features). All very decent in their own way. On the file system side of things, though, it’s another story. I’ve cobbled together some sort of ‘folders on the Cloud’ system over the years, but it’s seriously creaky. I break my own rules half the time!

Bearing that in mind, I was chuffed to bits to chance upon a whole new system recently – one that’s passed me by completely. It seems to be a particularly big hit across North American universities. It also has a large, active community online, sharing performance tweaks. And best of all – it uses completely free software.

Zotero and Obsidian

Zotero is a publications manager that you simply drag your e-material into. The app retrieves bibliographical information, renames files sensibly and stores a copy online for working cross-device. Even better, it’s capable of generating full bibliographies, so is a file store, reader and referencing tool all in one.

Obsidian is the note-taking side of this – a sleek, markdown-driven text editor that is beautifully minimalistic. It excels in creating hyperlinked notes, allowing you to build your own Wiki-style knowledge bank. But it dovetails beautifully into Zotero thanks to community plugins that allow you to import your PDF annotations directly into bibliographically pigeon-holed notes.

After resisting the temptation to kick myself for not spotting it sooner, I did a deep-dive into Zotero + Obsidian workflow how-tos, and it’s an academic revelation. A couple of community content creators are real stand-outs here – so much so that it’s best I let them do the talking rather than waffle any more. I’m learning this as I go along, and these are great places to start.

Workflow Training

Here’s where I started, more by chance YouTube search than anything else. Girl in Blue Music namechecks a lot of the other big Z+O content creators here, so it’s a good jumping point for newcomers.

From there, it’s worth exploring morganeua‘s vast selection of content, including numerous how-to videos and worked examples.

Once you’ve worked through those, you can graduate to full geek mode! Bryan Jenks pushes the system well beyond anything else I’ve seen, and likewise has a huge back catalogue of training vids. He layers styling and advanced templating onto the base, making for a slick, colour-coded, optimally managed research system.

I feel very late indeed to this workflow party. But if you are too, join the club – and let me know if you’ve found this useful too!

The Norwegian flag - the flag of Norway

#TikTokNorge – TikTok for Norwegian learners

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We’re slaves to the algorithm…. or are we? The great thing about TikTok is that you can engineer that algorithm with a bit of persistence. A search here, a like there, a comment somewhere else, and you subtly shift your TikTok-verse.

Of late, I’ve been nudging my own towards  serving up content that makes my aimless swiping a bit less aimless and a bit more, well, educational. And there’s a lot going on in #TikTokNorge! Mini lessons, everyday life, sketches and gags… Norwegian is well-covered on the platform – if you can uncover it first to coax onto your For You tab.

Here are some of the accounts helping me maintain and improve my own Norwegian lately – I hope you find a couple of gems in here too.

Norsk med Aria

Aria is a Norwegian teacher with a wealth of micro-lesson content on his feed, which he updates regularly. His videos are slick and well-edited, with a good balance between formal grammar tips and colloquial usage. He uses English as a presentation language, so it’s all accessible, too – a great place to start as a newcomer, as well as great revision and tips for more intermediate learners.

@norsk.med.aria

Ordering a coffee in Norwegian #norwegian #norsk

♬ original sound – Norsk med Aria – Norsk med Aria

Hilde Elise

Hilde Elise is an online Norwegian teacher who posts very regular monologues about life in Norway. She covers a huge range of topics from work and family to politics and current affairs, all at a level around A2-B1. This is delivered in clear, measured Bokmål too, so her videos are perfect for taking your language skills beyond simple sentences.

If you like her, also check out another teacher from her online school, Norsklærer Karense!

@hildeelise

Jeg elsker sola! #lærer #adjektiv #norskopplæring #morgen #norway

♬ original sound – Hilde Elise

Ola Norwegian

Ola, like Aria, uses English as his presentation language, giving his videos a more formal ‘classroom’ feel. But his content is top-notch, covering both grammar and word use. I’ve expanded my vocabulary with quite a few bits and pieces since following him.

@olanorwegian

Hvordan si “anyone”, “anywhere”, “anytime” osv? Jo: Du bruker frasen “som helst”! Men: En litt mer avansert frase er “however”. Vi kan nemlig IKKE si “Hvordan som helst”. Hmm. Har du svaret? Skriv i kommentarene! grammatikk norwegiancourse norskkurs norsk norwegian lærenorsk lærnorsk norskgrammatikk småprat smalltalk howtolearnnorwegian learnnorwegian norwegisch norskspråk norwegianlanguage norwegianculture norweski norway norge lifeinnorway explorenorway newtonorway noruega noruego oslo vocabulary vokabular

♬ original sound – Ola Norwegian – Ola Norwegian

Learn Norwegian with Preben

Preben is a worldly guy whose videos more often than not come from far-flung places well beyond Norway. But he has a focus on everyday Norwegian that is quite refreshing – casual, not overly analytical, and more like a mate telling you how to sound natural. For colloquial, idiomatic norsk, he’s your man!

https://www.tiktok.com/@norwegiancommunity/video/7533273676561550614

ilyantisocial.teaches

Like Preben, Ilya is a fan of the casual, colloquial approach to language. He’ll pick out everyday quirks and trip-ups that you won’t find in textbooks. His methods are a bit more organised, and you’ll get more chalk-and-talk in his videos, which may provide the yin to Preben’s yang!

@ilyantisocial.teaches

How to say hungry in Norwegian? How to say full in Norwegian? #norway #norwegian #languagelearning #language educational, speaking Norwegian, teacher things

♬ original sound – ilyantisocial.teaches

norwegian.with.tor

Tor was one of the first Norwegian content creators I discovered way back in the day on Instagram. Well, probably just a couple of years ago – an age in Internet terms. His content is perfect for the Insta reel format – fun, snappy sketches and gags with a learning slant. And he’s now popped up on TikTok, feeding your #NorwayTok algorithm with more micro-content.

https://www.tiktok.com/@norwegian.with.tor/video/7535920722074537238

So there you have it – six norsk content creators to transform your own algorithms with. Have I missed any of your own favourites? Let me know in the comments!

ElevenLabs Hits the Right Note: A.I. Songwriting for Language Learners

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In case you missed it, A.I. text-to-speech leader ElevenLabs is the latest platform to join the generative music scene – so language learners and teachers have another choice for creating original learning songs.

ElevenLabs’ Creative Platform ElevenMusic takes a much more structured approach to music creation that other platforms I’ve tried. Enter your prompt (or full lyrics), and it will build a song from block components – verse, chorus, bridge – just as you might construct one as a human writer. It makes for a much more natural-sounding track.

ElevenLabs music creation

ElevenLabs music creation

As you’d expect from voice experts ElevenLabs, the service copes with a wide range of languages and the diction is very convincing. A tad more so, I think, than the current iteration of the first big name on the block, Suno AI. No doubt the latter will have some tricks up its sleeve to keep up the pace – but for now, ElevenLabs is the place to go for quick and catchy learning song.

Anyway, here’s one I made earlier – a rather natty French rock and roll song about the Moon landings. Get those blue suede Moon boots on!

It’s definitely worth having a play on the site to see what you can come up with for you or your classes. ElevenLabs has a free tier, of course, so you can try it out straight away. [Note: that’s my wee affiliate link, so if you do sign up and hop on a higher tier later, you’re helping keep Polyglossic going!]

Generative Images Locally : Running Models on Your Machine

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I’ve written a fair bit about language models of late. This is a language blog, after all! But creating resources is about other visual elements, too. And just as you can run off text from local generative AI, you can create images locally, too.

For working on a computer, ComfyUI is a good bet. It’s a graphical dashboard for creating AI art with a huge array of customisation options. The fully-featuredness of it, admittedly, makes it a complex first intro to image generation. It’s interface, which takes a pipeline / modular format, takes a bit of getting used to. But it also comes with pre-defined workflows that mean you can just open it, prompt and go. There’s also a wide, active community that supports in online, so there’s plenty of help available.

Generate images locally - the ComfyUI interface

Generate images locally – the ComfyUI interface

At the more user-friendly end of it is Draw Things for Apple machines (unfortunately no Android yet). With a user interface much closer to art packages you’ll recognise, Draw Things allows you to download different models and prompt locally – and is available as an iOS app too. Obviously there’s a lot going on when you generate images, so it slugs along at quite a modest trot on my two-year-old iPad. But it gives you so much access to the buttons and knobs to tweak that it’s a great way to learn more about the generation process. Like ComfyUI, its complexity – once you get your head round it – actually teaches you a lot about image generation.

Of all the benefits of these apps, perhaps the greatest is again the environmental. You could fire up a browser and prompt one of the behemoths. But why crank up the heat on a distant data centre machine, when you can run locally? Many commercial generative models are far too powerful for what most people need.

Save power, and prompt locally. It’s more fun!

A swirl of IPA symbols in the ether. Do LLMs 'understand' phonology? And are they any good at translation?

Tencent’s Hunyuan-MT-7B, the Translation Whizz You Can Run Locally

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There’s been a lot of talk this week about a brand new translation model, Tencent’s Hunyuan-MT-7B. It’s a Large Language Model (LLM) trained to perform machine translation. And it’s caused a big stir by beating heftier (and heavier) models by Google and OpenAI in a recent event.

This is all the more remarkable given that it’s really quite a small model by LLM standards. Hunyuan actually manages its translation-beating feat packed into just 7 billion parameters (the information nodes that models learn from). Now that might sound a lot. But fewer usually means weaker, and the behemoths are nearing post-trillion param levels already.

So Hunyuan is small. But in spite of that, it can translate accurately and reliably – market-leader beatingly so – between over 30 languages, including some low-resource ones like Tibetan and Kazakh. And its footprint is truly tiny in LLM terms – it’s lightweight enough to run locally on a computer or even tablet, using inference software like LMStudio or PocketPal.

The model is available in various GGUF formats at Hugging Face. The 4-bit quantised version comes in at just over 4 GB, making it iPad-runnable. If you want greater fidelity, then 8-bit quantised is still only around 8 GB, easily handleable in LMStudio with a decent laptop spec.

So is it any good?

Well, I ran a few deliberately tricky English to German tasks through it, trying to find a weak spot. And honestly, it’s excellent – it produces idiomatic, native-quality translations that don’t sound clunky. What I found particularly impressive was its ability to paraphrase where a literal translation wouldn’t work.

There are plenty of use cases, even if you’re not looking for a translation engine for a full-blown app. Pocketising it means you have a top-notch multi-language translator to use offline, anywhere. For language learners – particularly those struggling with the lower-resource languages the model can handle with ease – it’s another source of native-quality text to learn from.

Find out more about the model at Hugging Face, and check out last week’s post for details on loading it onto your device!

Ultra-Mobile LLMs : Getting the Most from PocketPal

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If you were following along last week, I was deep into the territory of running open, small-scale Large Language Models (LLMs) locally on a laptop in the free LMStudio environment. There are lots of reasons you’d want to run these mini chatbots, including the educational, environmental, and security aspects.

I finished off with a very cursory mention of an even more mobile vehicle for these, PocketPal. This free, open source app (available on Google and iOS) allows for easy (no computer science degree required) searching, downloading and running LLMs on smartphones and tablets. And, despite the resource limitations of mobile devices compared with full computer hardware, they run surprisingly well.

PocketPal is such a powerful and unique tool, and definitely worth a spotlight of its own. So, this week, I thought I’d share some tips and tricks I’ve found for smooth running of these language models in your pocket.

Full-Fat LLMs?

First off, even small, compact models can be (as you’d expect) unwieldy and resource-heavy files. Compressed, self-contained LLM models are available as .gguf files from sources like Hugging Face, and they can be colossal. There’s a process you’ll hear mentioned a lot in the AI world called quantisation, which compresses models to varying degrees. Generally speaking, the more compression, the more poorly the model performs. But even the most highly compressed small models can weigh in at 2gb and above. After downloading them, these mammoth blobs then load into memory, ready to be prompted. That’s a lot of data for your system to be hanging onto!

That said, with disk space, a good internet connection, and decent RAM, it’s quite doable. On a newish MacBook, I was comfortably downloading and running .gguf files 8gb large and above in LMStudio. And you don’t need to downgrade your expectations too much to run models in PocketPal, either.

For reference, I’m using a 2023 iPad Pro with the M2 chip – quite a modest spec now – and a 2024 iPhone 16. On both of them, the sweet spot seems to be a .gguf size of around 4gb – you can go larger, but there’s a noticeable slowdown and sluggishness beyond that. A couple of the models I’ve been getting good, sensible and usable results from on mobile recently are:

  • Qwen3-4b-Instruct (8-bit quantised version) – 4.28gb
  • Llama-3.2-3B-Instruct (6-bit quantised version) – 3.26gb

The ‘instruct’ in those model names refers to the fact that they’ve been trained to follow instructions particularly keenly – one of the reasons they give such decent practical prompt responses with a small footprint.

Optimising PocketPal

Once you have them downloaded, there are a couple of things you can tweak in PocketPal to eke out even more performance.

The first is to head to the settings and switch on Metal, Apple’s hardware-accelerated API. Then, increase the “Layers on GPU” setting to around 80 or so – you can experiment with this to see what your system is happy with. But the performance improvement should be instantaneous, the LLM spitting out tokens at multiple times the default speed.

What’s happening with this change is that iOS is shifting some of the processing from the device’s CPU to the GPU, or graphical processing unit. That may seem odd, but modern graphics chips are capable of intense mathematical operations, and this small switch recruits them into doing some of the heavy work.

Additionally, on some recent devices, switching on “Flash Attention” can bring extra performance enhancements. This interacts with the way LLMs track how much weight to give certain tokens, and how that matrix is stored in memory during generation. It’s pot luck whether it will make a difference, depending on device spec, but I see a little boost.

Tweaking PocketPal’s settings to run LLMs more efficiently

Tweaking PocketPal’s settings to run LLMs more efficiently

Making Pals – Your Own Custom Bots

When you’re all up and running with your PocketPal LLMs, there’s another great feature you can play with to get very domain-specific results – “Pal” creation. Pals are just system prompts – instructions that set the boundaries and parameters for the conversation – in a nice wrapper. And you can be as specific as you want with them, instructing the LLM to behave as a language learning assistant, a nutrition expert, a habits coach, and such like – with as many rules and output notes as you see fit. It’s an easy way to turn a very generalised tool into something focused and with real-world application.

So that’s my PocketPal in-a-nutshell power guide. I hope you can see why it’s worth much more than just a cursory mention at the end of last week’s post! Tools like PocketPal and LMStudio put you right at the centre of LLM development, and I must admit it’s turned me into a models geek – I’m already looking forward to what new open LLMs will be unleashed next.

So what have you set your mobile models doing? Please share your tips and experiences in the comments!

Small LLMs

LLMs on Your Laptop

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I mentioned last week that I’m spending a lot of time with LLMs recently. I’m poking and prodding them to test their ‘understanding’ (inverted commas necessary there!) of phonology, in particular with non-standard speech and dialects.

And you’d be forgiven for thinking I’m just tapping my prompts into ChatGPT, Claude, Gemini or the other big commercial concerns. Mention AI, and those are the names people come up with. They’re the all-bells-and-whistles web-facing services that get all the public fanfare and newspaper column inches.

The thing is, that’s not all there is to Large Language Models. There’s a whole world of open source (or the slightly less open ‘open weights’) models out there. Some of them offshoots of those big names, while others less well-known. But you can download all of them to run offline on any reasonably-specced laptop.

LMStudio – LLMs on your laptop

Meet LMStudio – the multi-platform desktop app that allows you to install and interrogate LLMs locally. It all sounds terribly technical, but at its most basic use – a custom chatbot – you don’t need any special tech skills. Browsing, installing and chatting with models is all done via the tab-based interface. You can do much more with it – the option to run it as a local server is super useful for development and testing – but you don’t have to touch any of that.

Many of the models downloadable within LMStudio are small models – just a few gigabytes, rather than the behemoths behind GPT-5 and other headline-grabbing releases. They feature the same architecture as those big-hitters, though. And in many cases, they are trained to approach, even match, their performance on specific tasks like problem-solving or programming. You’ll even find reasoning models, that produce a ‘stepwise-thinking’ output, similar to platforms like Gemini.

A few recent models for download include:

  • Qwen3 4B Thinking – a really compact model (just over 2gb) which supports reasoning by default
  • OpenAI’s gpt-oss-20b – the AI giant’s open weights offering, released this August
  • Gemma 3 – Google’s multimodal model optimised for use on everyday devices
  • Mistral Small 3.2 – the French AI company’s open model, with vision capabilities

So why would you bother, when you can just fire up ChatGPT / Google / Claude in a few browser clicks?

LLMs locally – but why?

Well, from an academic standpoint, you have complete control over these models if you’re exploring their use cases in a particular field, like linguistics or language learning. You can set parameters like temperature, for instance – the degree of ‘creativity wobble’ the LLM has (0 being a very rigid none, and 1 being, well, basically insane). And if you can set parameters, you can report these in your findings, which allows others to replicate your experiments and build on your knowledge.

Small models also run on smaller hardware – so you can develop solutions that people don’t need a huge data centre for. If you do hit upon a use case or process that supports researchers, then it’s super easy for colleagues to access the technology, whatever their recourse to funding support.

Secondly, there’s the environmental impact. If the resource greed of colossal data centres is something that worries you (and there’s every indication that it should be a conversation we’re all having ), then running LLMs locally allows you to take advantage of them without heating up a server farm somewhere deep inside the US. The only thing running hot will be your laptop fan (it does growl a bit with the larger models – I take that as a sign to give it a rest for a bit!).

And talk of those US server farms leads on to the next point: data privacy. OpenAI recently caused waves with their suggestion that user conversations are not the confidential chats many assume them to be. If you’re not happy with your prompts and queries passing out of your control and into the data banks of a foreign state, then local LLMs offer not a little peace of mind too.

Give it a go!

The best thing? LMStudio is completely free. So download it, give it a spin, and see whether these much smaller-footprint models can give you what you need without entering the ecosystem of the online giants.

Lastly, don’t have a laptop? Well, you can also run LLMs locally on phones and tablets too. Free app PocketPal (on iOS and Android) runs like a cut-down version of LMStudio. Great for tinkering on the go!

A swirl of IPA symbols in the ether. Do LLMs 'understand' phonology? And are they any good at translation?

Do LLMs have phonological ‘understanding’?

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LLMs are everywhere just now. And as statistical word-crunchers, these large language models seem a tantalisingly good fit for linguistics work.

And, where there’s new tech, there’s new research: one of the big questions floating around in linguistics circles right now is whether large language models (LLMs) “understand” language systems in any meaningful way – at least any way that can be useful to research linguists.

LLMs doing the donkey work?

One truly exciting potential avenue is the use of LLMs to do the heavy lifting of massive corpus annotation. Language corpora can be huge – billions of words in some cases. And to be usefully searchable, those words have to be tagged with some kind of category information. For years, we’ve had logic-based Natural Language Processing (NLP) tech to do this, and for perhaps the most block-wise faculty of language – syntax – it’s done a generally grand, unthinking job.

But LLMs go one step beyond this. They not only demonstrate (or simulate) a more creative manipulation of language. Now, they have begun to incorporate thinking too. Many recent models,  such as the hot-off-the-press GPT-5, are already well along the production line of a new generation of high reasoning LLM models. These skills that are making them useful in other fields of linguistics, beyond syntax – fields where things like sentiment and intention come into play. Pragmatics is one area that has been a great fit, with one study into LLM tagging showing promising results.

The sounds behind the tokens

As for phonology, the linguistic field that deals with our mental representations of sound systems, the answer is a little more complicated.

On the one hand, LLMs are completely text-based. They don’t hear or produce sounds – they’re pattern matchers for strings of tokens – bits of words. But because written language does encode sound–meaning correspondences, they end up with a kind of latent ability to spot phonological patterns indirectly. For example, ask an LLM to generate rhyming words, or to apply a regular sound alternation like plural –s in English, and it usually does a decent job. In fact, one focus of a recent study was rhyming, and it found that, with some training, LLMs can approach a pretty humanlike level of rhyme generation.

On one level, that’s intuitive – it’s because orthography tends (largely) to reflect underlying phonotactics and morphophonology. Also, the sheer volume of data helps the model make the right generalisations – in those billions of pages of crunched training data, there are bound to be examples of the link. Where it gets shakier is with non-standard spellings, dialect writing, or novel words. Without clear orthographic cues, the model struggles to “hear” the system. You might see it overgeneralise, or miss distinctions that are obvious to a native speaker. In other words, it mimics phonological competence through text-based proxy, but it doesn’t have one.

It’s that ‘shakier’ competence I’m exploring in my own research right now. How easy is it to coax an understanding of non-standard phonology from an out-of-the-box LLM? Pre-training is key, finding wily ways to prime that mysterious ‘reasoning’ new models use.

Rough-Edged tools that need honing

So, do LLMs have phonological understanding?

Well, not in the sense of a human speaker with an embodied grammar. But what they do have is an uncanny knack for inferring patterns from writing, a kind of orthography-mediated phonology.

That makes them rough tools starting out, but potentially powerful assistants: not replacements for the linguist’s ear and analysis, but tools that can highlight patterns, make generalisation we might otherwise miss, and help us sift through mountains of messy data.

A finch flying above a beautiful landscape

Finch : Tiny Bird, Big Habits [Review]

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When I first saw a Finch ad on Instagram, I confess, I rolled my eyes. Yet another quirky productivity app wrapped up as a kid’s game and pitched to grown‑ups, I thought. Isn’t Insta awash with them lately? But curiosity won the day, and I’m honestly quite glad it did.

As you’ve probably guessed, Finch turns your self‑care and habit building into a gentle, gamified ritual – with a little birdie companion. It might seem a touch infantile, but don’t be fooled: its foundation rests on solid habit‑science, and yes, adults do love things that are fluffy and cute (well, I do anyway).

Finch is generous too – its free version offers custom goals, journaling, mood tracking and more, without forcing you to pay to access the essentials. I haven’t paid a penny to use it yet, but the range of function on the free tier has been more than enough to keep me using it.

Why It Works

  • It starts small. When you first set it up, the suggested goals are self-care easy wins – drink more water, get outside at least once day – things to get you used to the app environment. Want to journal, stretch, or simply “get out of bed”? Go ahead and make it count.
  • Flexible goal‑setting for grown‑ups. Once you’ve got used to the interface, you can go to town setting your own goals – even on the free tier. I’ve added language learning daily tactics, university reading goals and all sorts – I almost feel guilty that I’m doing all this without a subscription!
  • Gentle gamification. As you check off goals, your bird gains energy, goes on charming adventures, and earns “Rainbow Stones” for adorning its nest. It’s rewarding without being punishing. And of course, also streak-building is part of the ecosystem, your Finch never dies if you miss a day (God forbid).
  • Supportive, not prescriptive. Other users highlight how the app strikes the right tone: compassionate rather than preachy. Some users with ADHD, anxiety or depression say its warmth makes self‑care feel doable.
  • Friend‑based encouragement. You can buddy up with a friend on a single goal (or more) without exposing your progress to a social feed. It’s discreet, pressure‑free support. For a laugh, I added a pal on the “drink more water” goal. We laugh about it, but it’s actually not a bad habit to develop, is it?

Final Verdict

Finch is a cosy, surprisingly effective habit app wrapped in feathers and whimsy. It’s kind to energy-drained minds, flexible enough for real lives, and – despite coming at me via the dreaded Insta ads – far more than a passing gimmick.

If you’ve ever felt wary of habit tools that feel too serious or demanding, Finch might just surprise you. And if nothing else, the little bird and its gentle cheer-on can make daily tasks feel a bit more doable – and dare I say it, sweet.

Finch is available as a free download on all the usual platforms –
find out more at their website here!