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diy succulent mix

diy succulent mix Molly's Gritty Mix for Cactus & Bonsai

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Description

diy succulent mix Molly's Gritty Mix for Cactus & BonsaiQuick answer: what is Molly's Succulent Mix? For: succulents, cacti, bonsai, Haworthia, Echeveria, Sedum, Jade, and any arid environment plant. What's in it: high mineral gritty blend of pumice, lava rock, and crushed bark. Low organic matter by design. Why it works: succulent roots are built to drink fast and dry out fast. The gritty structure drains in seconds and holds zero standing water, so roots don't rot. Pre rinsed and pH balanced straight

Quick answer: what is Molly's Succulent Mix?

  • For: succulents, cacti, bonsai, Haworthia, Echeveria, Sedum, Jade, and any arid-environment plant.
  • What's in it: high-mineral gritty blend of pumice, lava rock, and crushed bark. Low organic matter by design.
  • Why it works: succulent roots are built to drink fast and dry out fast. The gritty structure drains in seconds and holds zero standing water, so roots don't rot.
  • Pre-rinsed and pH-balanced straight from the bag. No salt flush required.
  • Bonsai-safe. The grit profile matches what serious bonsai growers blend by hand from akadama, pumice, and lava.

More plant-specific guidance: Ultimate guide to growing succulents indoors, Potting soil vs potting mix.

Succulents and cacti evolved in arid, mineral-rich environments where water moves through gritty substrate in seconds. Their roots are built to drink fast and dry out fast. Standard potting soil holds moisture for days, suffocates the roots, and rots them from the bottom up. The fix is a high-mineral, low-organic, gritty mix.

Molly's Succulent Mix is engineered to mimic native desert and rocky-slope substrates. A blend of pumice, lava rock, and a small amount of organic matter that drains in seconds and forces the soak-and-dry watering rhythm succulents need.

The gritty-mix philosophy

Most "succulent soil" sold at garden centres is regular potting soil with sand mixed in. That's not what these plants want. The right mix is roughly 70% mineral aggregate (pumice and lava rock) and 30% structural organic (coir, charcoal). Water hits the surface and runs through within seconds. Roots get a brief, intense drink, then dry conditions for the next 1 to 2 weeks. That's how succulents stay alive in pots.

What's in the bag

  • Pumice (volcanic, lightweight): the mineral backbone. Holds a tiny amount of water inside its porous structure, but lets the rest drain freely.
  • Lava rock (red lava): chunky drainage and heat retention. Roots love the warmth differential it creates.
  • Coir fiber (small percentage): just enough organic to retain a little humidity and prevent the mix from drying to a brick. Not enough to compromise drainage.
  • Horticultural charcoal: filters salts from tap water (succulents are surprisingly sensitive to mineral buildup).
  • Calcitic limestone (trace): buffers pH to the slightly alkaline range (6.5 to 7.5) most desert succulents prefer.

Low peat content, no worm castings (succulents don't want a nutrient flush), no commercial fertilizer. The whole mix is intentionally lean.

Plants this is for

Designed for succulents and cacti:

  • Echeveria, Sedum, Crassula (jade), Sempervivum: the classic rosette succulents.
  • Haworthia, Gasteria: they prefer slightly more shade but want the same gritty drainage.
  • Aloe (vera and others): medicinal succulents, this mix prevents the rot they're prone to in heavier soils.
  • Most cacti: Mammillaria, Echinopsis, Opuntia, San Pedro, golden barrel.
  • Lithops (living stones): require fast drainage to stay alive year-round; this mix is well-suited.
  • Bonsai with high drainage needs: juniper, pine, and certain deciduous bonsai work well.
  • Caudex plants: Adenium, Pachypodium, and other swollen-stem species that need fast drainage at the base.

Not for: tropical "succulent-looking" plants like Hoya, Christmas cactus (Schlumbergera), or Easter cactus, which actually prefer humidity-retaining mixes. For those, use Molly's Aroid Mix.

Watering with gritty mix

The right rhythm: soak and dry. Water deeply, then wait until the mix is bone-dry before watering again.

  1. Wait until the top 2 to 3 inches feel completely dry. For most succulents in standard 4 to 6 inch pots, that's every 10 to 21 days indoors.
  2. Water until liquid runs clearly out the drainage holes. Don't dribble. Soak.
  3. Discard any water in the saucer. Do not let the pot sit in standing water.
  4. Wait. The plant will let you know when it's thirsty (slight wrinkling of leaves, lighter pot weight).

In winter, water roughly half as often. Most succulents go dormant or semi-dormant.

FAQ

Why is this so heavy compared to other succulent soil?

Because it's mostly minerals, not peat or coco coir. The weight is what makes it work. Light bag means light drainage, which is the opposite of what succulents need.

Can I use this for bonsai?

For tropical bonsai, no, they want a moisture-retentive aroid-style mix. For drought-tolerant bonsai (juniper, pine, certain deciduous species), yes, this mix or a 50/50 blend with finer organics works well.

Will the mix break down or stay porous over time?

Stays porous. The mineral components (pumice, lava rock, charcoal) don't decompose. The small organic fraction breaks down slowly. Most succulents in this mix can go 2 to 3 years before repotting.

Should I add fertilizer?

Sparingly. Succulents are slow growers and don't need much. A diluted (~1/4 strength) cactus-specific fertilizer once during the growing season (spring) is plenty for most species.

Packaged in a heat-sealed resealable bag. New formula released April 2026, see the formula release announcement for details on what changed.

Related care guide

Watering, light, and repotting fundamentals for succulents and cacti.

→ Read the Succulent & Cactus Care guide

Have questions? Read the Molly's Succulent Mix FAQ for detailed information on watering, repotting, and which succulents this mix works best for.

New: the complete soil guide

Not sure if you need cactus soil or succulent soil? They are the same thing. Read: Best Soil for Succulents and Cactus →

Not sure which mix your plant needs?

Take our free 60-second Soil Finder quiz → Diagnose the problem and get the exact Molly's mix and amount for your plant, plus 10% off.

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Kirsten
Belleville, US
★★★★★ 5
Holds a decent amount of jewelry!
Color: Carbonized Brown, Color: Carbonized Brown
I was quite impressed with this little jewelry box. Although it is on the smaller side, it utilizes every bit of the storage space available really well. I’d ultimately love to get a bigger armoire- as it is, this jewelry box contains what I wear most often, but I have a larger collection than this particular jewelry box can hold- my plan is to find a larger jewelry armoire that resembles what my mother had because I loved that one and then passed this one down to my daughter who loves it. For its size, it does absolutely hold a lot. I definitely underestimated how much it would hold. I love that there are drawers and well. I would love to see the ring area hinged so that I don’t have to reposition it when I’m done grabbing my rings, I think it’s a really cool, unique way to approach that particular area. I love that every little bit at this jewelry box is designed to have utility. I hate wasting space and time and I love good organization so it’s been really nice being able to pack as much as I can in there. The top opens up to space for earrings and other miscellaneous items. There are both open and more structured components. And the space for bracelets rotates, which is really nice- I didn’t realize that it rotated and I was a little bit worried that I was gonna constantly knock things down while I was reaching through or something. There is lots of room inside both doors for necklaces, and it fits a lot more than I thought it would. The wood stain is a really pretty kind of ashy natural stain- the sort of grey tint is really nice and it’s gorgeous. I’m not a huge fan of mirrors as far as the front goes, but I do have an artist in house who is really good at coming up with stuff for this, just a little ways to put art in your every day, so I’ll probably have her paint over. The jewelry box also doesn’t take much space up at all. While I am looking for something with a little bit larger footprint, I don’t necessarily want to waste a bunch of real estate in the meantime so I’m really pleased with how compact it is. This is a great little jewelry box - as I mentioned it doesn’t house all of my jewelry, but that’s because my collection is mostly heirloom and I don’t want to take it out from where it is right now. If it were larger, I would probably do so but for now it just houses my everyday items and a little bit extra. I think it’s great and I’m super happy with it!
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Reviewed in the United States on March 17, 2026
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Bozeman, US
★★★★★ 5
Excellent book, possibly currently unique in coverage of latest ideas
This book is possibly currently unique in its coverage of the latest ideas in the field of deep learning -- and it is a very convenient and good survey of fundamental concepts (linear algebra, optimization, performance metrics, activation function types), different network types (multi-layer perceptron, convolutional neural networks, and recurrent neural networks), practical considerations (data set, training and validation, implementation), and applications (comments on existing real-world/commercial uses). The final 235 pages of the content portion of the book is dedicated to topics in "Deep Learning Research", and these topics are truly at the current frontier. Another reviewer said that one could gain the same knowledge of cutting-edge research by reading all of the latest papers (from academia and industry), but the "research" section of this book offers the following: Selection of the most notable research by the very experienced authors of the book, and collection of similar research in to a broader discussion of themes, and the additional insights. The book covers very advanced and new ideas currently being explored, and it is very nice to be able to have a consistent and coherent presentation of all of those ideas. However, the book is also packed with valuable observations and pointers about more basic aspects of deep learning implementations and practices -- and such commentary is in depth and includes substantial analysis and mathematical derivation (in an intuitive presentation that often includes graphs illustrating the phenomenon). As someone with an intermediate level of knowledge and experience of neural networks, I am really grateful for this book, because seems like the ideal resource for learning cutting-edge ideas and practices, with context. The book has excellent scope and depth, and I am confident that anyone with a solid background in linear algebra, calculus, statistics, and general machine learning, and basic neural networks (multi-layer perceptrons) will find this book to be very exciting and perhaps unique in its ability to take the reader to the next level and a new frontier. I was personally excited to learn about the idea of representing the dependencies of intermediate quantities by directed graphs, and how this can be used to perform calculations for recurrent neural networks efficiently. And I think the long chapter on recurrent neural networks is very helpful. Having said all of this, I think only people with significant working knowledge and experience with neural networks and mathematics -- people whose academic or professional focus has been neural networks for at least a year or two -- would benefit from this book. This book answers a lot of the deeper questions that one is likely to have while developing a solid understanding of the fundamentals, and that's one of the book's tremendous values, but this book assumes an understanding of the fundamentals (but does briskly cover the basics). I think this book is a perfect follow-up book for the excellent book "Neural Network Design (2nd edition)" by Hagan, Demuth, Beale, and de Jesus, and I highly recommend the latter for gaining the solid background needed to have a thrilling experience with the "Deep Learning" book. In summary, I am very glad this "Deep Learning" book was written, and I think the "Deep Learning" book will be a great benefit to a lot of people, and to the evolution of the field.
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Reviewed in the United States on April 18, 2017
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Zygerian99
Carnegie, US
★★★★★ 5
The definitive guide to becoming a researcher in the field
Format: Hardcover
This is not a coding book. I see a lot of negative reviews around the expectation that this book would teach the reader how to quickly build machine learning systems and write code. This book is not for that audience. If you just want to build applications, don't worry about how deep learning works. It's akin to needing to understand how an engine works just to drive a car. If you are looking for a coding resource, try: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_4?keywords=machine+learning+tensorflow&qid=1579608765&sr=8-4 . And even with that book, the material still goes far beyond what you need - use it as a light reference. I bought this book as an aspiring machine learning researcher, and towards that end, it is the best resource available in print (still true as of 2020). For instance: The first 5 chapters are timeless. These are things that were mostly established 20 or 30 years ago and beyond and are mostly STEM fundamentals at this point. There are whole textbooks dedicated to each of those chapters, but the authors provide a quick refresher and overview of probably 80% of what you'll encounter in deep learning. If you haven't previously learned each of these subtopics, you'll probably want to study them individually since they are the key to innovating (linear algebra, probability & stats, numerical computation, machine learning fundamentals). Chapters 6 thru 9 are the foundation of deep learning. We're about 12 years into seeing rapid change in the deep learning space, yet all of these principles and techniques still hold (many recent innovations are still relying on Convolutional models in 2020, which is the most layered/complex topics in those chapters). Therefore, I'd wager that these chapters are also fairly stable knowledge that is worth internalizing if you want to be deeply involved in the future of machine learning. Chapters after 9 are mostly experimental topics, and many of them are already the wrong strategies for optimal results. But there are interesting ideas in here that you'll often encounter in the wild, so it's good exposure to various topics. But probably not worth much of your time. And lastly, there is good history in here from people who know the space intimately. It's a good way to piece together the developments and learn the lexicon of deep learning so you can have intelligent conversation with experts.
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Reviewed in the United States on January 21, 2020
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Shannon
Omaha, US
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
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Reviewed in the United States on November 30, 2025
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Verified Purchase
William P Ross
Battle Creek, US
★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning. There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s. The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read. There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning. The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique. Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
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Reviewed in the United States on March 15, 2017

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